Overview

Dataset statistics

Number of variables200
Number of observations509599
Missing cells1198763
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory809.9 MiB
Average record size in memory1.6 KiB

Variable types

Text2
Boolean45
Categorical153

Alerts

company_revenue_bucket is highly overall correlated with revenue_Over 10B and 6 other fieldsHigh correlation
country_top is highly overall correlated with country_top_AU and 14 other fieldsHigh correlation
country_top_AU is highly overall correlated with country_top and 1 other fieldsHigh correlation
country_top_BR is highly overall correlated with country_top and 1 other fieldsHigh correlation
country_top_CA is highly overall correlated with country_top and 1 other fieldsHigh correlation
country_top_DE is highly overall correlated with country_top and 1 other fieldsHigh correlation
country_top_FR is highly overall correlated with country_top and 1 other fieldsHigh correlation
country_top_GB is highly overall correlated with country_top and 1 other fieldsHigh correlation
country_top_IN is highly overall correlated with country_top and 1 other fieldsHigh correlation
country_top_Missing is highly overall correlated with country_top and 2 other fieldsHigh correlation
country_top_NL is highly overall correlated with country_top and 1 other fieldsHigh correlation
country_top_Other is highly overall correlated with country_top and 1 other fieldsHigh correlation
country_top_US is highly overall correlated with country_top and 4 other fieldsHigh correlation
crossbeam_product10_customer is highly overall correlated with crossbeam_product11_customer and 22 other fieldsHigh correlation
crossbeam_product11_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product12_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product13_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product14_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product15_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product16_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product17_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product18_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product19_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product1_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product20_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product21_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product22_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product23_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product2_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product3_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product4_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product5_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product6_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product7_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product8_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
crossbeam_product9_customer is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
dnb_founded_time_grouped is highly overall correlated with founded_After 2000 and 2 other fieldsHigh correlation
founded_After 2000 is highly overall correlated with dnb_founded_time_grouped and 1 other fieldsHigh correlation
founded_Before 2000 is highly overall correlated with dnb_founded_time_groupedHigh correlation
founded_nan is highly overall correlated with dnb_founded_time_grouped and 1 other fieldsHigh correlation
has_crossbeam_data is highly overall correlated with crossbeam_product10_customer and 22 other fieldsHigh correlation
has_hg_data is highly overall correlated with hg_product_100 and 123 other fieldsHigh correlation
hg_product_100 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_101 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_102 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_103 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_104 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_105 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_106 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_107 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_108 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_109 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_110 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_111 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_112 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_113 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_114 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_115 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_116 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_117 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_118 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_119 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_120 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_121 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_122 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_123 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_124 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_125 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_126 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_127 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_128 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_129 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_130 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_131 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_132 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_133 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_134 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_135 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_136 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_137 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_138 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_139 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_140 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_141 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_142 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_143 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_144 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_145 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_146 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_147 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_148 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_27 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_28 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_29 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_30 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_31 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_32 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_33 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_34 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_35 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_36 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_37 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_38 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_39 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_40 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_41 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_42 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_43 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_44 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_45 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_46 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_47 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_48 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_49 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_50 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_51 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_52 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_53 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_54 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_55 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_56 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_57 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_58 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_59 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_60 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_61 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_62 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_63 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_64 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_65 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_66 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_67 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_68 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_69 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_70 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_71 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_72 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_73 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_74 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_75 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_76 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_77 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_78 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_79 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_80 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_81 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_82 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_83 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_84 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_85 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_86 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_87 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_88 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_89 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_90 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_91 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_92 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_93 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_94 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_95 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_96 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_97 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_98 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
hg_product_99 is highly overall correlated with has_hg_data and 123 other fieldsHigh correlation
industry_1. Tech - Computer systems design and related services is highly overall correlated with industry_groupedHigh correlation
industry_2. Tech - Software Publisher is highly overall correlated with industry_groupedHigh correlation
industry_3. Finance is highly overall correlated with industry_groupedHigh correlation
industry_4. Consulting is highly overall correlated with industry_groupedHigh correlation
industry_5. Retail is highly overall correlated with industry_groupedHigh correlation
industry_6. Manufacturing is highly overall correlated with industry_groupedHigh correlation
industry_7. Wholesale Trade is highly overall correlated with industry_groupedHigh correlation
industry_8. Healthcare is highly overall correlated with industry_groupedHigh correlation
industry_9. Other is highly overall correlated with industry_groupedHigh correlation
industry_grouped is highly overall correlated with industry_1. Tech - Computer systems design and related services and 9 other fieldsHigh correlation
industry_nan is highly overall correlated with country_top and 2 other fieldsHigh correlation
is_arr_over_12k is highly overall correlated with is_current_customer and 1 other fieldsHigh correlation
is_current_customer is highly overall correlated with is_arr_over_12k and 1 other fieldsHigh correlation
is_self_service is highly overall correlated with is_arr_over_12k and 1 other fieldsHigh correlation
revenue_Over 10B is highly overall correlated with company_revenue_bucketHigh correlation
revenue_Under 100M is highly overall correlated with company_revenue_bucketHigh correlation
revenue_Under 10B is highly overall correlated with company_revenue_bucketHigh correlation
revenue_Under 10M is highly overall correlated with company_revenue_bucketHigh correlation
revenue_Under 1B is highly overall correlated with company_revenue_bucket and 124 other fieldsHigh correlation
revenue_Under 1M is highly overall correlated with company_revenue_bucketHigh correlation
revenue_nan is highly overall correlated with company_revenue_bucket and 124 other fieldsHigh correlation
state is highly overall correlated with country_top and 23 other fieldsHigh correlation
state_top is highly overall correlated with country_top_US and 12 other fieldsHigh correlation
state_top_CA is highly overall correlated with state and 1 other fieldsHigh correlation
state_top_CO is highly overall correlated with state and 1 other fieldsHigh correlation
state_top_FL is highly overall correlated with state and 1 other fieldsHigh correlation
state_top_GA is highly overall correlated with state and 1 other fieldsHigh correlation
state_top_IL is highly overall correlated with state and 1 other fieldsHigh correlation
state_top_MA is highly overall correlated with state and 1 other fieldsHigh correlation
state_top_Missing is highly overall correlated with country_top and 4 other fieldsHigh correlation
state_top_NY is highly overall correlated with state and 1 other fieldsHigh correlation
state_top_Other is highly overall correlated with country_top and 4 other fieldsHigh correlation
state_top_TX is highly overall correlated with state and 1 other fieldsHigh correlation
state_top_WA is highly overall correlated with state and 1 other fieldsHigh correlation
is_current_customer is highly imbalanced (87.3%)Imbalance
is_self_service is highly imbalanced (91.0%)Imbalance
is_arr_over_12k is highly imbalanced (90.9%)Imbalance
has_hg_data is highly imbalanced (55.0%)Imbalance
hg_product_27 is highly imbalanced (71.1%)Imbalance
hg_product_28 is highly imbalanced (68.8%)Imbalance
hg_product_29 is highly imbalanced (70.3%)Imbalance
hg_product_30 is highly imbalanced (70.4%)Imbalance
hg_product_31 is highly imbalanced (67.5%)Imbalance
hg_product_32 is highly imbalanced (65.3%)Imbalance
hg_product_33 is highly imbalanced (66.7%)Imbalance
hg_product_34 is highly imbalanced (66.5%)Imbalance
hg_product_35 is highly imbalanced (70.7%)Imbalance
hg_product_36 is highly imbalanced (68.4%)Imbalance
hg_product_37 is highly imbalanced (69.2%)Imbalance
hg_product_38 is highly imbalanced (67.7%)Imbalance
hg_product_39 is highly imbalanced (68.2%)Imbalance
hg_product_40 is highly imbalanced (70.9%)Imbalance
hg_product_41 is highly imbalanced (68.7%)Imbalance
hg_product_42 is highly imbalanced (68.7%)Imbalance
hg_product_43 is highly imbalanced (70.4%)Imbalance
hg_product_44 is highly imbalanced (68.4%)Imbalance
hg_product_45 is highly imbalanced (67.8%)Imbalance
hg_product_46 is highly imbalanced (71.0%)Imbalance
hg_product_47 is highly imbalanced (65.6%)Imbalance
hg_product_48 is highly imbalanced (65.3%)Imbalance
hg_product_49 is highly imbalanced (69.8%)Imbalance
hg_product_50 is highly imbalanced (67.7%)Imbalance
hg_product_51 is highly imbalanced (69.5%)Imbalance
hg_product_52 is highly imbalanced (69.9%)Imbalance
hg_product_53 is highly imbalanced (70.4%)Imbalance
hg_product_54 is highly imbalanced (71.0%)Imbalance
hg_product_55 is highly imbalanced (66.7%)Imbalance
hg_product_56 is highly imbalanced (71.0%)Imbalance
hg_product_57 is highly imbalanced (68.9%)Imbalance
hg_product_58 is highly imbalanced (71.1%)Imbalance
hg_product_59 is highly imbalanced (71.1%)Imbalance
hg_product_60 is highly imbalanced (70.9%)Imbalance
hg_product_61 is highly imbalanced (71.1%)Imbalance
hg_product_62 is highly imbalanced (69.2%)Imbalance
hg_product_63 is highly imbalanced (67.6%)Imbalance
hg_product_64 is highly imbalanced (70.9%)Imbalance
hg_product_65 is highly imbalanced (69.3%)Imbalance
hg_product_66 is highly imbalanced (70.5%)Imbalance
hg_product_67 is highly imbalanced (65.1%)Imbalance
hg_product_68 is highly imbalanced (68.5%)Imbalance
hg_product_69 is highly imbalanced (67.9%)Imbalance
hg_product_70 is highly imbalanced (70.0%)Imbalance
hg_product_71 is highly imbalanced (70.6%)Imbalance
hg_product_72 is highly imbalanced (69.0%)Imbalance
hg_product_73 is highly imbalanced (65.0%)Imbalance
hg_product_74 is highly imbalanced (69.5%)Imbalance
hg_product_75 is highly imbalanced (68.9%)Imbalance
hg_product_76 is highly imbalanced (68.1%)Imbalance
hg_product_77 is highly imbalanced (68.0%)Imbalance
hg_product_78 is highly imbalanced (69.7%)Imbalance
hg_product_79 is highly imbalanced (68.8%)Imbalance
hg_product_80 is highly imbalanced (69.8%)Imbalance
hg_product_81 is highly imbalanced (69.1%)Imbalance
hg_product_82 is highly imbalanced (71.1%)Imbalance
hg_product_83 is highly imbalanced (66.1%)Imbalance
hg_product_84 is highly imbalanced (70.4%)Imbalance
hg_product_85 is highly imbalanced (71.1%)Imbalance
hg_product_86 is highly imbalanced (70.5%)Imbalance
hg_product_87 is highly imbalanced (65.2%)Imbalance
hg_product_88 is highly imbalanced (68.4%)Imbalance
hg_product_89 is highly imbalanced (70.2%)Imbalance
hg_product_90 is highly imbalanced (70.0%)Imbalance
hg_product_91 is highly imbalanced (71.1%)Imbalance
hg_product_92 is highly imbalanced (70.6%)Imbalance
hg_product_93 is highly imbalanced (71.1%)Imbalance
hg_product_94 is highly imbalanced (71.0%)Imbalance
hg_product_95 is highly imbalanced (69.7%)Imbalance
hg_product_96 is highly imbalanced (68.8%)Imbalance
hg_product_97 is highly imbalanced (70.8%)Imbalance
hg_product_98 is highly imbalanced (70.6%)Imbalance
hg_product_99 is highly imbalanced (70.6%)Imbalance
hg_product_100 is highly imbalanced (70.1%)Imbalance
hg_product_101 is highly imbalanced (70.3%)Imbalance
hg_product_102 is highly imbalanced (66.1%)Imbalance
hg_product_103 is highly imbalanced (68.5%)Imbalance
hg_product_104 is highly imbalanced (70.5%)Imbalance
hg_product_105 is highly imbalanced (69.2%)Imbalance
hg_product_106 is highly imbalanced (70.9%)Imbalance
hg_product_107 is highly imbalanced (70.8%)Imbalance
hg_product_108 is highly imbalanced (71.1%)Imbalance
hg_product_109 is highly imbalanced (71.0%)Imbalance
hg_product_110 is highly imbalanced (70.9%)Imbalance
hg_product_111 is highly imbalanced (71.1%)Imbalance
hg_product_112 is highly imbalanced (69.5%)Imbalance
hg_product_113 is highly imbalanced (71.1%)Imbalance
hg_product_114 is highly imbalanced (69.6%)Imbalance
hg_product_115 is highly imbalanced (71.0%)Imbalance
hg_product_116 is highly imbalanced (70.6%)Imbalance
hg_product_117 is highly imbalanced (70.9%)Imbalance
hg_product_118 is highly imbalanced (71.1%)Imbalance
hg_product_119 is highly imbalanced (69.1%)Imbalance
hg_product_120 is highly imbalanced (70.7%)Imbalance
hg_product_121 is highly imbalanced (71.0%)Imbalance
hg_product_122 is highly imbalanced (71.1%)Imbalance
hg_product_123 is highly imbalanced (70.2%)Imbalance
hg_product_124 is highly imbalanced (66.4%)Imbalance
hg_product_125 is highly imbalanced (65.7%)Imbalance
hg_product_126 is highly imbalanced (70.5%)Imbalance
hg_product_127 is highly imbalanced (70.6%)Imbalance
hg_product_128 is highly imbalanced (71.1%)Imbalance
hg_product_129 is highly imbalanced (65.1%)Imbalance
hg_product_130 is highly imbalanced (70.9%)Imbalance
hg_product_131 is highly imbalanced (70.8%)Imbalance
hg_product_132 is highly imbalanced (68.1%)Imbalance
hg_product_133 is highly imbalanced (70.9%)Imbalance
hg_product_134 is highly imbalanced (70.9%)Imbalance
hg_product_135 is highly imbalanced (71.1%)Imbalance
hg_product_136 is highly imbalanced (71.1%)Imbalance
hg_product_137 is highly imbalanced (70.6%)Imbalance
hg_product_138 is highly imbalanced (70.8%)Imbalance
hg_product_139 is highly imbalanced (68.7%)Imbalance
hg_product_140 is highly imbalanced (67.3%)Imbalance
hg_product_141 is highly imbalanced (70.3%)Imbalance
hg_product_142 is highly imbalanced (70.6%)Imbalance
hg_product_143 is highly imbalanced (68.5%)Imbalance
hg_product_144 is highly imbalanced (65.7%)Imbalance
hg_product_145 is highly imbalanced (67.6%)Imbalance
hg_product_146 is highly imbalanced (70.9%)Imbalance
hg_product_147 is highly imbalanced (70.7%)Imbalance
hg_product_148 is highly imbalanced (71.0%)Imbalance
country_top_AU is highly imbalanced (80.5%)Imbalance
country_top_BR is highly imbalanced (88.9%)Imbalance
country_top_CA is highly imbalanced (79.5%)Imbalance
country_top_DE is highly imbalanced (75.8%)Imbalance
country_top_FR is highly imbalanced (79.7%)Imbalance
country_top_GB is highly imbalanced (57.3%)Imbalance
country_top_IN is highly imbalanced (65.6%)Imbalance
country_top_Missing is highly imbalanced (60.5%)Imbalance
country_top_NL is highly imbalanced (86.9%)Imbalance
state_top_CA is highly imbalanced (59.2%)Imbalance
state_top_CO is highly imbalanced (90.9%)Imbalance
state_top_FL is highly imbalanced (85.7%)Imbalance
state_top_GA is highly imbalanced (91.6%)Imbalance
state_top_IL is highly imbalanced (88.5%)Imbalance
state_top_MA is highly imbalanced (89.2%)Imbalance
state_top_NY is highly imbalanced (75.4%)Imbalance
state_top_TX is highly imbalanced (82.5%)Imbalance
state_top_WA is highly imbalanced (90.6%)Imbalance
industry_2. Tech - Software Publisher is highly imbalanced (90.0%)Imbalance
industry_3. Finance is highly imbalanced (69.3%)Imbalance
industry_4. Consulting is highly imbalanced (76.7%)Imbalance
industry_5. Retail is highly imbalanced (72.8%)Imbalance
industry_6. Manufacturing is highly imbalanced (60.9%)Imbalance
industry_7. Wholesale Trade is highly imbalanced (75.8%)Imbalance
industry_8. Healthcare is highly imbalanced (80.7%)Imbalance
revenue_Over 10B is highly imbalanced (95.5%)Imbalance
revenue_Under 100M is highly imbalanced (86.7%)Imbalance
revenue_Under 10B is highly imbalanced (88.0%)Imbalance
revenue_Under 10M is highly imbalanced (96.8%)Imbalance
revenue_Under 1B is highly imbalanced (77.1%)Imbalance
revenue_Under 1M is highly imbalanced (98.4%)Imbalance
revenue_nan is highly imbalanced (59.2%)Imbalance
company_revenue_bucket has 467922 (91.8%) missing valuesMissing
country has 39738 (7.8%) missing valuesMissing
state has 319941 (62.8%) missing valuesMissing
industry_grouped has 69660 (13.7%) missing valuesMissing
dnb_founded_time_grouped has 301502 (59.2%) missing valuesMissing
account_id has unique valuesUnique

Reproduction

Analysis started2023-12-31 21:09:11.154655
Analysis finished2023-12-31 21:26:21.106452
Duration17 minutes and 9.95 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

account_id
Text

UNIQUE 

Distinct509599
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size36.4 MiB
2023-12-31T13:26:21.683857image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters9172782
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique509599 ?
Unique (%)100.0%

Sample

1st row001PY000002SuJDYA0
2nd row0011G00000nEkzYQAS
3rd row0011G00000qFM5EQAW
4th row0011G00000tKtSbQAK
5th row0011G00000tLivvQAC
ValueCountFrequency (%)
001py000002sujdya0 1
 
< 0.1%
0011g00000kvcljqae 1
 
< 0.1%
0011g00000tlivvqac 1
 
< 0.1%
0011g00000uogzkqa2 1
 
< 0.1%
0011g00000ufx52qaa 1
 
< 0.1%
0011g00000xa4ekqa0 1
 
< 0.1%
0011g0000120dn1qai 1
 
< 0.1%
0011g0000135y3dqaa 1
 
< 0.1%
001ho000013liqniau 1
 
< 0.1%
001ho000015sspjiac 1
 
< 0.1%
Other values (509589) 509589
> 99.9%
2023-12-31T13:26:22.593832image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3533017
38.5%
1 1074416
 
11.7%
A 611024
 
6.7%
Q 491331
 
5.4%
G 472809
 
5.2%
Y 137982
 
1.5%
I 130298
 
1.4%
w 102497
 
1.1%
o 100477
 
1.1%
n 88058
 
1.0%
Other values (52) 2430873
26.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000518
54.5%
Uppercase Letter 2846060
31.0%
Lowercase Letter 1326204
 
14.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 611024
21.5%
Q 491331
17.3%
G 472809
16.6%
Y 137982
 
4.8%
I 130298
 
4.6%
H 86188
 
3.0%
U 85973
 
3.0%
C 78209
 
2.7%
E 76664
 
2.7%
K 68702
 
2.4%
Other values (16) 606880
21.3%
Lowercase Letter
ValueCountFrequency (%)
w 102497
 
7.7%
o 100477
 
7.6%
n 88058
 
6.6%
h 76780
 
5.8%
k 75634
 
5.7%
q 65966
 
5.0%
u 60877
 
4.6%
j 60814
 
4.6%
g 56766
 
4.3%
y 48260
 
3.6%
Other values (16) 590075
44.5%
Decimal Number
ValueCountFrequency (%)
0 3533017
70.7%
1 1074416
 
21.5%
2 78484
 
1.6%
3 60244
 
1.2%
7 49855
 
1.0%
4 44146
 
0.9%
5 43696
 
0.9%
9 41396
 
0.8%
6 38676
 
0.8%
8 36588
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 5000518
54.5%
Latin 4172264
45.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 611024
 
14.6%
Q 491331
 
11.8%
G 472809
 
11.3%
Y 137982
 
3.3%
I 130298
 
3.1%
w 102497
 
2.5%
o 100477
 
2.4%
n 88058
 
2.1%
H 86188
 
2.1%
U 85973
 
2.1%
Other values (42) 1865627
44.7%
Common
ValueCountFrequency (%)
0 3533017
70.7%
1 1074416
 
21.5%
2 78484
 
1.6%
3 60244
 
1.2%
7 49855
 
1.0%
4 44146
 
0.9%
5 43696
 
0.9%
9 41396
 
0.8%
6 38676
 
0.8%
8 36588
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9172782
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3533017
38.5%
1 1074416
 
11.7%
A 611024
 
6.7%
Q 491331
 
5.4%
G 472809
 
5.2%
Y 137982
 
1.5%
I 130298
 
1.4%
w 102497
 
1.1%
o 100477
 
1.1%
n 88058
 
1.0%
Other values (52) 2430873
26.5%

is_current_customer
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
500720 
True
 
8879
ValueCountFrequency (%)
False 500720
98.3%
True 8879
 
1.7%
2023-12-31T13:26:22.828286image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

is_self_service
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
500720 
1.0
 
5843
-1.0
 
3036

Length

Max length4
Median length3
Mean length3.0059576
Min length3

Characters and Unicode

Total characters1531833
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 500720
98.3%
1.0 5843
 
1.1%
-1.0 3036
 
0.6%

Length

2023-12-31T13:26:22.997637image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:23.158261image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 500720
98.3%
1.0 8879
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 1010319
66.0%
. 509599
33.3%
1 8879
 
0.6%
- 3036
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
66.5%
Other Punctuation 509599
33.3%
Dash Punctuation 3036
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1010319
99.1%
1 8879
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3036
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1531833
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1010319
66.0%
. 509599
33.3%
1 8879
 
0.6%
- 3036
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1531833
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1010319
66.0%
. 509599
33.3%
1 8879
 
0.6%
- 3036
 
0.2%

is_arr_over_12k
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
500720 
-1.0
 
5462
1.0
 
3417

Length

Max length4
Median length3
Mean length3.0107182
Min length3

Characters and Unicode

Total characters1534259
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 500720
98.3%
-1.0 5462
 
1.1%
1.0 3417
 
0.7%

Length

2023-12-31T13:26:23.330493image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:23.488835image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 500720
98.3%
1.0 8879
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 1010319
65.9%
. 509599
33.2%
1 8879
 
0.6%
- 5462
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
66.4%
Other Punctuation 509599
33.2%
Dash Punctuation 5462
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1010319
99.1%
1 8879
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5462
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1534259
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1010319
65.9%
. 509599
33.2%
1 8879
 
0.6%
- 5462
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1534259
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1010319
65.9%
. 509599
33.2%
1 8879
 
0.6%
- 5462
 
0.4%

company_revenue_bucket
Categorical

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing467922
Missing (%)91.8%
Memory size31.2 MiB
Under 1B
18939 
Under 100M
9412 
Under 10B
8303 
Over 10B
2533 
Under 10M
 
1713

Length

Max length10
Median length8
Mean length8.6919884
Min length8

Characters and Unicode

Total characters362256
Distinct characters12
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnder 10B
2nd rowOver 10B
3rd rowUnder 1B
4th rowOver 10B
5th rowUnder 10B

Common Values

ValueCountFrequency (%)
Under 1B 18939
 
3.7%
Under 100M 9412
 
1.8%
Under 10B 8303
 
1.6%
Over 10B 2533
 
0.5%
Under 10M 1713
 
0.3%
Under 1M 777
 
0.2%
(Missing) 467922
91.8%

Length

2023-12-31T13:26:23.672919image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:23.873232image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
under 39144
47.0%
1b 18939
22.7%
10b 10836
 
13.0%
100m 9412
 
11.3%
over 2533
 
3.0%
10m 1713
 
2.1%
1m 777
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e 41677
11.5%
r 41677
11.5%
41677
11.5%
1 41677
11.5%
U 39144
10.8%
n 39144
10.8%
d 39144
10.8%
0 31373
8.7%
B 29775
8.2%
M 11902
 
3.3%
Other values (2) 5066
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 164175
45.3%
Uppercase Letter 83354
23.0%
Decimal Number 73050
20.2%
Space Separator 41677
 
11.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 41677
25.4%
r 41677
25.4%
n 39144
23.8%
d 39144
23.8%
v 2533
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
U 39144
47.0%
B 29775
35.7%
M 11902
 
14.3%
O 2533
 
3.0%
Decimal Number
ValueCountFrequency (%)
1 41677
57.1%
0 31373
42.9%
Space Separator
ValueCountFrequency (%)
41677
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 247529
68.3%
Common 114727
31.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 41677
16.8%
r 41677
16.8%
U 39144
15.8%
n 39144
15.8%
d 39144
15.8%
B 29775
12.0%
M 11902
 
4.8%
O 2533
 
1.0%
v 2533
 
1.0%
Common
ValueCountFrequency (%)
41677
36.3%
1 41677
36.3%
0 31373
27.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 362256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 41677
11.5%
r 41677
11.5%
41677
11.5%
1 41677
11.5%
U 39144
10.8%
n 39144
10.8%
d 39144
10.8%
0 31373
8.7%
B 29775
8.2%
M 11902
 
3.3%
Other values (2) 5066
 
1.4%

country
Text

MISSING 

Distinct255
Distinct (%)0.1%
Missing39738
Missing (%)7.8%
Memory size27.7 MiB
2023-12-31T13:26:24.337958image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Length

Max length14
Median length2
Mean length2.0003278
Min length2

Characters and Unicode

Total characters939876
Distinct characters55
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)< 0.1%

Sample

1st rowCH
2nd rowNL
3rd rowRO
4th rowIN
5th rowUS
ValueCountFrequency (%)
us 192675
41.0%
gb 44376
 
9.4%
in 32745
 
7.0%
de 20320
 
4.3%
ca 16384
 
3.5%
fr 16160
 
3.4%
au 15384
 
3.3%
nl 9280
 
2.0%
br 7508
 
1.6%
jp 6742
 
1.4%
Other values (239) 108290
23.0%
2023-12-31T13:26:25.033504image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 213208
22.7%
S 211762
22.5%
B 56918
 
6.1%
N 54200
 
5.8%
G 52456
 
5.6%
I 50599
 
5.4%
E 45211
 
4.8%
A 44023
 
4.7%
R 32257
 
3.4%
C 30342
 
3.2%
Other values (45) 148900
15.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 939685
> 99.9%
Lowercase Letter 177
 
< 0.1%
Decimal Number 8
 
< 0.1%
Space Separator 3
 
< 0.1%
Math Symbol 2
 
< 0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 213208
22.7%
S 211762
22.5%
B 56918
 
6.1%
N 54200
 
5.8%
G 52456
 
5.6%
I 50599
 
5.4%
E 45211
 
4.8%
A 44023
 
4.7%
R 32257
 
3.4%
C 30342
 
3.2%
Other values (16) 148709
15.8%
Lowercase Letter
ValueCountFrequency (%)
e 24
13.6%
a 19
10.7%
n 17
9.6%
t 15
 
8.5%
l 14
 
7.9%
r 14
 
7.9%
d 11
 
6.2%
i 11
 
6.2%
s 7
 
4.0%
u 7
 
4.0%
Other values (11) 38
21.5%
Decimal Number
ValueCountFrequency (%)
2 2
25.0%
7 2
25.0%
1 2
25.0%
0 1
12.5%
8 1
12.5%
Space Separator
ValueCountFrequency (%)
3
100.0%
Math Symbol
ValueCountFrequency (%)
√ 2
100.0%
Other Symbol
ValueCountFrequency (%)
© 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 939862
> 99.9%
Common 14
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 213208
22.7%
S 211762
22.5%
B 56918
 
6.1%
N 54200
 
5.8%
G 52456
 
5.6%
I 50599
 
5.4%
E 45211
 
4.8%
A 44023
 
4.7%
R 32257
 
3.4%
C 30342
 
3.2%
Other values (37) 148886
15.8%
Common
ValueCountFrequency (%)
3
21.4%
√ 2
14.3%
2 2
14.3%
7 2
14.3%
1 2
14.3%
© 1
 
7.1%
0 1
 
7.1%
8 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 939872
> 99.9%
Math Operators 2
 
< 0.1%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 213208
22.7%
S 211762
22.5%
B 56918
 
6.1%
N 54200
 
5.8%
G 52456
 
5.6%
I 50599
 
5.4%
E 45211
 
4.8%
A 44023
 
4.7%
R 32257
 
3.4%
C 30342
 
3.2%
Other values (42) 148896
15.8%
Math Operators
ValueCountFrequency (%)
√ 2
100.0%
None
ValueCountFrequency (%)
© 1
50.0%
õ 1
50.0%

state
Categorical

HIGH CORRELATION  MISSING 

Distinct50
Distinct (%)< 0.1%
Missing319941
Missing (%)62.8%
Memory size30.2 MiB
CA
41553 
NY
20829 
TX
13310 
FL
10321 
IL
 
7908
Other values (45)
95737 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters379316
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMN
2nd rowMD
3rd rowFL
4th rowNH
5th rowWA

Common Values

ValueCountFrequency (%)
CA 41553
 
8.2%
NY 20829
 
4.1%
TX 13310
 
2.6%
FL 10321
 
2.0%
IL 7908
 
1.6%
MA 7304
 
1.4%
WA 6142
 
1.2%
CO 5923
 
1.2%
GA 5336
 
1.0%
PA 5184
 
1.0%
Other values (40) 65848
 
12.9%
(Missing) 319941
62.8%

Length

2023-12-31T13:26:25.276709image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 41553
21.9%
ny 20829
 
11.0%
tx 13310
 
7.0%
fl 10321
 
5.4%
il 7908
 
4.2%
ma 7304
 
3.9%
wa 6142
 
3.2%
co 5923
 
3.1%
ga 5336
 
2.8%
pa 5184
 
2.7%
Other values (40) 65848
34.7%

Most occurring characters

ValueCountFrequency (%)
A 77434
20.4%
C 55145
14.5%
N 41859
11.0%
Y 22368
 
5.9%
T 21303
 
5.6%
M 20816
 
5.5%
L 20295
 
5.4%
I 18617
 
4.9%
O 16631
 
4.4%
X 13310
 
3.5%
Other values (14) 71538
18.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 379316
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 77434
20.4%
C 55145
14.5%
N 41859
11.0%
Y 22368
 
5.9%
T 21303
 
5.6%
M 20816
 
5.5%
L 20295
 
5.4%
I 18617
 
4.9%
O 16631
 
4.4%
X 13310
 
3.5%
Other values (14) 71538
18.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 379316
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 77434
20.4%
C 55145
14.5%
N 41859
11.0%
Y 22368
 
5.9%
T 21303
 
5.6%
M 20816
 
5.5%
L 20295
 
5.4%
I 18617
 
4.9%
O 16631
 
4.4%
X 13310
 
3.5%
Other values (14) 71538
18.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 379316
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 77434
20.4%
C 55145
14.5%
N 41859
11.0%
Y 22368
 
5.9%
T 21303
 
5.6%
M 20816
 
5.5%
L 20295
 
5.4%
I 18617
 
4.9%
O 16631
 
4.4%
X 13310
 
3.5%
Other values (14) 71538
18.9%

industry_grouped
Categorical

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)< 0.1%
Missing69660
Missing (%)13.7%
Memory size35.0 MiB
9. Other
227796 
1. Tech - Computer systems design and related services
59896 
6. Manufacturing
39102 
3. Finance
27997 
5. Retail
23768 
Other values (4)
61380 

Length

Max length54
Median length8
Mean length16.308479
Min length8

Characters and Unicode

Total characters7174736
Distinct characters43
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9. Other
2nd row9. Other
3rd row9. Other
4th row3. Finance
5th row9. Other

Common Values

ValueCountFrequency (%)
9. Other 227796
44.7%
1. Tech - Computer systems design and related services 59896
 
11.8%
6. Manufacturing 39102
 
7.7%
3. Finance 27997
 
5.5%
5. Retail 23768
 
4.7%
7. Wholesale Trade 20318
 
4.0%
4. Consulting 19317
 
3.8%
8. Healthcare 15149
 
3.0%
2. Tech - Software Publisher 6596
 
1.3%
(Missing) 69660
 
13.7%

Length

2023-12-31T13:26:25.467474image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:25.671335image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
9 227796
17.0%
other 227796
17.0%
tech 66492
 
5.0%
66492
 
5.0%
1 59896
 
4.5%
computer 59896
 
4.5%
systems 59896
 
4.5%
design 59896
 
4.5%
and 59896
 
4.5%
related 59896
 
4.5%
Other values (17) 391304
29.2%

Most occurring characters

ValueCountFrequency (%)
899317
 
12.5%
e 869769
 
12.1%
t 511416
 
7.1%
r 495245
 
6.9%
. 439939
 
6.1%
s 405607
 
5.7%
h 336351
 
4.7%
a 327291
 
4.6%
n 292624
 
4.1%
i 236572
 
3.3%
Other values (33) 2360605
32.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4795704
66.8%
Space Separator 899317
 
12.5%
Uppercase Letter 533345
 
7.4%
Other Punctuation 439939
 
6.1%
Decimal Number 439939
 
6.1%
Dash Punctuation 66492
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 869769
18.1%
t 511416
10.7%
r 495245
10.3%
s 405607
8.5%
h 336351
 
7.0%
a 327291
 
6.8%
n 292624
 
6.1%
i 236572
 
4.9%
c 208636
 
4.4%
d 200006
 
4.2%
Other values (11) 912187
19.0%
Uppercase Letter
ValueCountFrequency (%)
O 227796
42.7%
T 86810
 
16.3%
C 79213
 
14.9%
M 39102
 
7.3%
F 27997
 
5.2%
R 23768
 
4.5%
W 20318
 
3.8%
H 15149
 
2.8%
S 6596
 
1.2%
P 6596
 
1.2%
Decimal Number
ValueCountFrequency (%)
9 227796
51.8%
1 59896
 
13.6%
6 39102
 
8.9%
3 27997
 
6.4%
5 23768
 
5.4%
7 20318
 
4.6%
4 19317
 
4.4%
8 15149
 
3.4%
2 6596
 
1.5%
Space Separator
ValueCountFrequency (%)
899317
100.0%
Other Punctuation
ValueCountFrequency (%)
. 439939
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 66492
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5329049
74.3%
Common 1845687
 
25.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 869769
16.3%
t 511416
 
9.6%
r 495245
 
9.3%
s 405607
 
7.6%
h 336351
 
6.3%
a 327291
 
6.1%
n 292624
 
5.5%
i 236572
 
4.4%
O 227796
 
4.3%
c 208636
 
3.9%
Other values (21) 1417742
26.6%
Common
ValueCountFrequency (%)
899317
48.7%
. 439939
23.8%
9 227796
 
12.3%
- 66492
 
3.6%
1 59896
 
3.2%
6 39102
 
2.1%
3 27997
 
1.5%
5 23768
 
1.3%
7 20318
 
1.1%
4 19317
 
1.0%
Other values (2) 21745
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7174736
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
899317
 
12.5%
e 869769
 
12.1%
t 511416
 
7.1%
r 495245
 
6.9%
. 439939
 
6.1%
s 405607
 
5.7%
h 336351
 
4.7%
a 327291
 
4.6%
n 292624
 
4.1%
i 236572
 
3.3%
Other values (33) 2360605
32.9%

has_crossbeam_data
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
326935 
True
182664 
ValueCountFrequency (%)
False 326935
64.2%
True 182664
35.8%
2023-12-31T13:26:25.891009image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

crossbeam_product1_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.3 MiB
0.0
326470 
-1.0
182904 
1.0
 
225

Length

Max length4
Median length3
Mean length3.3589175
Min length3

Characters and Unicode

Total characters1711701
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 326470
64.1%
-1.0 182904
35.9%
1.0 225
 
< 0.1%

Length

2023-12-31T13:26:26.069872image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:26.235795image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 326470
64.1%
1.0 183129
35.9%

Most occurring characters

ValueCountFrequency (%)
0 836069
48.8%
. 509599
29.8%
1 183129
 
10.7%
- 182904
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
59.5%
Other Punctuation 509599
29.8%
Dash Punctuation 182904
 
10.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 836069
82.0%
1 183129
 
18.0%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 182904
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1711701
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 836069
48.8%
. 509599
29.8%
1 183129
 
10.7%
- 182904
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1711701
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 836069
48.8%
. 509599
29.8%
1 183129
 
10.7%
- 182904
 
10.7%

crossbeam_product2_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.3 MiB
0.0
326470 
-1.0
182923 
1.0
 
206

Length

Max length4
Median length3
Mean length3.3589548
Min length3

Characters and Unicode

Total characters1711720
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 326470
64.1%
-1.0 182923
35.9%
1.0 206
 
< 0.1%

Length

2023-12-31T13:26:26.416867image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:26.578735image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 326470
64.1%
1.0 183129
35.9%

Most occurring characters

ValueCountFrequency (%)
0 836069
48.8%
. 509599
29.8%
1 183129
 
10.7%
- 182923
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
59.5%
Other Punctuation 509599
29.8%
Dash Punctuation 182923
 
10.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 836069
82.0%
1 183129
 
18.0%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 182923
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1711720
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 836069
48.8%
. 509599
29.8%
1 183129
 
10.7%
- 182923
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1711720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 836069
48.8%
. 509599
29.8%
1 183129
 
10.7%
- 182923
 
10.7%

crossbeam_product3_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.3 MiB
0.0
326470 
-1.0
182840 
1.0
 
289

Length

Max length4
Median length3
Mean length3.3587919
Min length3

Characters and Unicode

Total characters1711637
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 326470
64.1%
-1.0 182840
35.9%
1.0 289
 
0.1%

Length

2023-12-31T13:26:26.755351image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:26.918563image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 326470
64.1%
1.0 183129
35.9%

Most occurring characters

ValueCountFrequency (%)
0 836069
48.8%
. 509599
29.8%
1 183129
 
10.7%
- 182840
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
59.5%
Other Punctuation 509599
29.8%
Dash Punctuation 182840
 
10.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 836069
82.0%
1 183129
 
18.0%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 182840
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1711637
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 836069
48.8%
. 509599
29.8%
1 183129
 
10.7%
- 182840
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1711637
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 836069
48.8%
. 509599
29.8%
1 183129
 
10.7%
- 182840
 
10.7%

crossbeam_product4_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.3 MiB
0.0
326470 
-1.0
183058 
1.0
 
71

Length

Max length4
Median length3
Mean length3.3592197
Min length3

Characters and Unicode

Total characters1711855
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 326470
64.1%
-1.0 183058
35.9%
1.0 71
 
< 0.1%

Length

2023-12-31T13:26:27.095839image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:27.269413image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 326470
64.1%
1.0 183129
35.9%

Most occurring characters

ValueCountFrequency (%)
0 836069
48.8%
. 509599
29.8%
1 183129
 
10.7%
- 183058
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
59.5%
Other Punctuation 509599
29.8%
Dash Punctuation 183058
 
10.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 836069
82.0%
1 183129
 
18.0%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 183058
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1711855
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 836069
48.8%
. 509599
29.8%
1 183129
 
10.7%
- 183058
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1711855
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 836069
48.8%
. 509599
29.8%
1 183129
 
10.7%
- 183058
 
10.7%

crossbeam_product5_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.3 MiB
0.0
326470 
-1.0
182311 
1.0
 
818

Length

Max length4
Median length3
Mean length3.3577538
Min length3

Characters and Unicode

Total characters1711108
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 326470
64.1%
-1.0 182311
35.8%
1.0 818
 
0.2%

Length

2023-12-31T13:26:27.459163image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:27.621054image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 326470
64.1%
1.0 183129
35.9%

Most occurring characters

ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 182311
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
59.6%
Other Punctuation 509599
29.8%
Dash Punctuation 182311
 
10.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 836069
82.0%
1 183129
 
18.0%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 182311
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1711108
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 182311
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1711108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 182311
 
10.7%

crossbeam_product6_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.3 MiB
0.0
326470 
-1.0
175464 
1.0
 
7665

Length

Max length4
Median length3
Mean length3.3443178
Min length3

Characters and Unicode

Total characters1704261
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 326470
64.1%
-1.0 175464
34.4%
1.0 7665
 
1.5%

Length

2023-12-31T13:26:27.801408image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:27.964105image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 326470
64.1%
1.0 183129
35.9%

Most occurring characters

ValueCountFrequency (%)
0 836069
49.1%
. 509599
29.9%
1 183129
 
10.7%
- 175464
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
59.8%
Other Punctuation 509599
29.9%
Dash Punctuation 175464
 
10.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 836069
82.0%
1 183129
 
18.0%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 175464
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1704261
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 836069
49.1%
. 509599
29.9%
1 183129
 
10.7%
- 175464
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1704261
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 836069
49.1%
. 509599
29.9%
1 183129
 
10.7%
- 175464
 
10.3%

crossbeam_product7_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.3 MiB
0.0
326470 
-1.0
182444 
1.0
 
685

Length

Max length4
Median length3
Mean length3.3580148
Min length3

Characters and Unicode

Total characters1711241
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 326470
64.1%
-1.0 182444
35.8%
1.0 685
 
0.1%

Length

2023-12-31T13:26:28.141120image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:28.305801image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 326470
64.1%
1.0 183129
35.9%

Most occurring characters

ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 182444
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
59.6%
Other Punctuation 509599
29.8%
Dash Punctuation 182444
 
10.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 836069
82.0%
1 183129
 
18.0%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 182444
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1711241
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 182444
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1711241
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 182444
 
10.7%

crossbeam_product8_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.3 MiB
0.0
326470 
-1.0
182150 
1.0
 
979

Length

Max length4
Median length3
Mean length3.3574379
Min length3

Characters and Unicode

Total characters1710947
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 326470
64.1%
-1.0 182150
35.7%
1.0 979
 
0.2%

Length

2023-12-31T13:26:28.484106image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:28.645764image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 326470
64.1%
1.0 183129
35.9%

Most occurring characters

ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 182150
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
59.6%
Other Punctuation 509599
29.8%
Dash Punctuation 182150
 
10.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 836069
82.0%
1 183129
 
18.0%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 182150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1710947
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 182150
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1710947
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 182150
 
10.6%

crossbeam_product9_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.3 MiB
0.0
326470 
-1.0
182927 
1.0
 
202

Length

Max length4
Median length3
Mean length3.3589626
Min length3

Characters and Unicode

Total characters1711724
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 326470
64.1%
-1.0 182927
35.9%
1.0 202
 
< 0.1%

Length

2023-12-31T13:26:28.825376image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:28.989371image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 326470
64.1%
1.0 183129
35.9%

Most occurring characters

ValueCountFrequency (%)
0 836069
48.8%
. 509599
29.8%
1 183129
 
10.7%
- 182927
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
59.5%
Other Punctuation 509599
29.8%
Dash Punctuation 182927
 
10.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 836069
82.0%
1 183129
 
18.0%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 182927
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1711724
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 836069
48.8%
. 509599
29.8%
1 183129
 
10.7%
- 182927
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1711724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 836069
48.8%
. 509599
29.8%
1 183129
 
10.7%
- 182927
 
10.7%

crossbeam_product10_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.3 MiB
0.0
326470 
-1.0
181577 
1.0
 
1552

Length

Max length4
Median length3
Mean length3.3563135
Min length3

Characters and Unicode

Total characters1710374
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 326470
64.1%
-1.0 181577
35.6%
1.0 1552
 
0.3%

Length

2023-12-31T13:26:29.164716image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:29.327214image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 326470
64.1%
1.0 183129
35.9%

Most occurring characters

ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 181577
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
59.6%
Other Punctuation 509599
29.8%
Dash Punctuation 181577
 
10.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 836069
82.0%
1 183129
 
18.0%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 181577
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1710374
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 181577
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1710374
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 181577
 
10.6%

crossbeam_product11_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.3 MiB
0.0
326470 
-1.0
182540 
1.0
 
589

Length

Max length4
Median length3
Mean length3.3582032
Min length3

Characters and Unicode

Total characters1711337
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 326470
64.1%
-1.0 182540
35.8%
1.0 589
 
0.1%

Length

2023-12-31T13:26:29.505219image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:29.667974image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 326470
64.1%
1.0 183129
35.9%

Most occurring characters

ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 182540
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
59.6%
Other Punctuation 509599
29.8%
Dash Punctuation 182540
 
10.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 836069
82.0%
1 183129
 
18.0%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 182540
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1711337
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 182540
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1711337
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 182540
 
10.7%

crossbeam_product12_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.3 MiB
0.0
326470 
-1.0
182097 
1.0
 
1032

Length

Max length4
Median length3
Mean length3.3573339
Min length3

Characters and Unicode

Total characters1710894
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 326470
64.1%
-1.0 182097
35.7%
1.0 1032
 
0.2%

Length

2023-12-31T13:26:29.844228image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:30.005050image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 326470
64.1%
1.0 183129
35.9%

Most occurring characters

ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 182097
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
59.6%
Other Punctuation 509599
29.8%
Dash Punctuation 182097
 
10.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 836069
82.0%
1 183129
 
18.0%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 182097
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1710894
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 182097
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1710894
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 182097
 
10.6%

crossbeam_product13_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.3 MiB
0.0
326470 
-1.0
183007 
1.0
 
122

Length

Max length4
Median length3
Mean length3.3591196
Min length3

Characters and Unicode

Total characters1711804
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 326470
64.1%
-1.0 183007
35.9%
1.0 122
 
< 0.1%

Length

2023-12-31T13:26:30.182571image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:30.342551image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 326470
64.1%
1.0 183129
35.9%

Most occurring characters

ValueCountFrequency (%)
0 836069
48.8%
. 509599
29.8%
1 183129
 
10.7%
- 183007
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
59.5%
Other Punctuation 509599
29.8%
Dash Punctuation 183007
 
10.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 836069
82.0%
1 183129
 
18.0%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 183007
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1711804
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 836069
48.8%
. 509599
29.8%
1 183129
 
10.7%
- 183007
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1711804
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 836069
48.8%
. 509599
29.8%
1 183129
 
10.7%
- 183007
 
10.7%

crossbeam_product14_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.3 MiB
0.0
326470 
-1.0
181861 
1.0
 
1268

Length

Max length4
Median length3
Mean length3.3568708
Min length3

Characters and Unicode

Total characters1710658
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 326470
64.1%
-1.0 181861
35.7%
1.0 1268
 
0.2%

Length

2023-12-31T13:26:30.519858image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:30.680006image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 326470
64.1%
1.0 183129
35.9%

Most occurring characters

ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 181861
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
59.6%
Other Punctuation 509599
29.8%
Dash Punctuation 181861
 
10.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 836069
82.0%
1 183129
 
18.0%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 181861
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1710658
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 181861
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1710658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 181861
 
10.6%

crossbeam_product15_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.3 MiB
0.0
326470 
-1.0
177973 
1.0
 
5156

Length

Max length4
Median length3
Mean length3.3492413
Min length3

Characters and Unicode

Total characters1706770
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 326470
64.1%
-1.0 177973
34.9%
1.0 5156
 
1.0%

Length

2023-12-31T13:26:30.858122image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:31.017419image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 326470
64.1%
1.0 183129
35.9%

Most occurring characters

ValueCountFrequency (%)
0 836069
49.0%
. 509599
29.9%
1 183129
 
10.7%
- 177973
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
59.7%
Other Punctuation 509599
29.9%
Dash Punctuation 177973
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 836069
82.0%
1 183129
 
18.0%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 177973
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1706770
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 836069
49.0%
. 509599
29.9%
1 183129
 
10.7%
- 177973
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1706770
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 836069
49.0%
. 509599
29.9%
1 183129
 
10.7%
- 177973
 
10.4%

crossbeam_product16_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.3 MiB
0.0
326470 
-1.0
182849 
1.0
 
280

Length

Max length4
Median length3
Mean length3.3588096
Min length3

Characters and Unicode

Total characters1711646
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 326470
64.1%
-1.0 182849
35.9%
1.0 280
 
0.1%

Length

2023-12-31T13:26:31.193560image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:31.356565image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 326470
64.1%
1.0 183129
35.9%

Most occurring characters

ValueCountFrequency (%)
0 836069
48.8%
. 509599
29.8%
1 183129
 
10.7%
- 182849
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
59.5%
Other Punctuation 509599
29.8%
Dash Punctuation 182849
 
10.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 836069
82.0%
1 183129
 
18.0%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 182849
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1711646
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 836069
48.8%
. 509599
29.8%
1 183129
 
10.7%
- 182849
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1711646
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 836069
48.8%
. 509599
29.8%
1 183129
 
10.7%
- 182849
 
10.7%

crossbeam_product17_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.3 MiB
0.0
326470 
-1.0
182875 
1.0
 
254

Length

Max length4
Median length3
Mean length3.3588606
Min length3

Characters and Unicode

Total characters1711672
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 326470
64.1%
-1.0 182875
35.9%
1.0 254
 
< 0.1%

Length

2023-12-31T13:26:31.531914image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:31.693623image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 326470
64.1%
1.0 183129
35.9%

Most occurring characters

ValueCountFrequency (%)
0 836069
48.8%
. 509599
29.8%
1 183129
 
10.7%
- 182875
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
59.5%
Other Punctuation 509599
29.8%
Dash Punctuation 182875
 
10.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 836069
82.0%
1 183129
 
18.0%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 182875
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1711672
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 836069
48.8%
. 509599
29.8%
1 183129
 
10.7%
- 182875
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1711672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 836069
48.8%
. 509599
29.8%
1 183129
 
10.7%
- 182875
 
10.7%

crossbeam_product18_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.3 MiB
0.0
326470 
-1.0
182398 
1.0
 
731

Length

Max length4
Median length3
Mean length3.3579246
Min length3

Characters and Unicode

Total characters1711195
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 326470
64.1%
-1.0 182398
35.8%
1.0 731
 
0.1%

Length

2023-12-31T13:26:31.868375image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:32.029751image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 326470
64.1%
1.0 183129
35.9%

Most occurring characters

ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 182398
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
59.6%
Other Punctuation 509599
29.8%
Dash Punctuation 182398
 
10.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 836069
82.0%
1 183129
 
18.0%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 182398
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1711195
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 182398
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1711195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 182398
 
10.7%

crossbeam_product19_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.3 MiB
0.0
326470 
-1.0
181481 
1.0
 
1648

Length

Max length4
Median length3
Mean length3.3561251
Min length3

Characters and Unicode

Total characters1710278
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 326470
64.1%
-1.0 181481
35.6%
1.0 1648
 
0.3%

Length

2023-12-31T13:26:32.547732image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:32.708380image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 326470
64.1%
1.0 183129
35.9%

Most occurring characters

ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 181481
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
59.6%
Other Punctuation 509599
29.8%
Dash Punctuation 181481
 
10.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 836069
82.0%
1 183129
 
18.0%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 181481
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1710278
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 181481
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1710278
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 181481
 
10.6%

crossbeam_product20_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.3 MiB
0.0
326470 
-1.0
172693 
1.0
 
10436

Length

Max length4
Median length3
Mean length3.3388802
Min length3

Characters and Unicode

Total characters1701490
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 326470
64.1%
-1.0 172693
33.9%
1.0 10436
 
2.0%

Length

2023-12-31T13:26:32.889548image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:33.068309image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 326470
64.1%
1.0 183129
35.9%

Most occurring characters

ValueCountFrequency (%)
0 836069
49.1%
. 509599
30.0%
1 183129
 
10.8%
- 172693
 
10.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
59.9%
Other Punctuation 509599
30.0%
Dash Punctuation 172693
 
10.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 836069
82.0%
1 183129
 
18.0%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 172693
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1701490
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 836069
49.1%
. 509599
30.0%
1 183129
 
10.8%
- 172693
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1701490
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 836069
49.1%
. 509599
30.0%
1 183129
 
10.8%
- 172693
 
10.1%

crossbeam_product21_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.3 MiB
0.0
326470 
-1.0
182080 
1.0
 
1049

Length

Max length4
Median length3
Mean length3.3573005
Min length3

Characters and Unicode

Total characters1710877
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 326470
64.1%
-1.0 182080
35.7%
1.0 1049
 
0.2%

Length

2023-12-31T13:26:33.258925image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:33.428779image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 326470
64.1%
1.0 183129
35.9%

Most occurring characters

ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 182080
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
59.6%
Other Punctuation 509599
29.8%
Dash Punctuation 182080
 
10.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 836069
82.0%
1 183129
 
18.0%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 182080
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1710877
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 182080
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1710877
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 182080
 
10.6%

crossbeam_product22_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.3 MiB
0.0
326470 
-1.0
182627 
1.0
 
502

Length

Max length4
Median length3
Mean length3.3583739
Min length3

Characters and Unicode

Total characters1711424
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 326470
64.1%
-1.0 182627
35.8%
1.0 502
 
0.1%

Length

2023-12-31T13:26:33.609877image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:33.777434image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 326470
64.1%
1.0 183129
35.9%

Most occurring characters

ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 182627
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
59.6%
Other Punctuation 509599
29.8%
Dash Punctuation 182627
 
10.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 836069
82.0%
1 183129
 
18.0%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 182627
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1711424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 182627
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1711424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 836069
48.9%
. 509599
29.8%
1 183129
 
10.7%
- 182627
 
10.7%

crossbeam_product23_customer
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.3 MiB
0.0
326470 
-1.0
152515 
1.0
 
30614

Length

Max length4
Median length3
Mean length3.2992843
Min length3

Characters and Unicode

Total characters1681312
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 326470
64.1%
-1.0 152515
29.9%
1.0 30614
 
6.0%

Length

2023-12-31T13:26:33.958130image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:34.122518image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 326470
64.1%
1.0 183129
35.9%

Most occurring characters

ValueCountFrequency (%)
0 836069
49.7%
. 509599
30.3%
1 183129
 
10.9%
- 152515
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
60.6%
Other Punctuation 509599
30.3%
Dash Punctuation 152515
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 836069
82.0%
1 183129
 
18.0%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 152515
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1681312
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 836069
49.7%
. 509599
30.3%
1 183129
 
10.9%
- 152515
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1681312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 836069
49.7%
. 509599
30.3%
1 183129
 
10.9%
- 152515
 
9.1%

dnb_founded_time_grouped
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing301502
Missing (%)59.2%
Memory size31.8 MiB
After 2000
138001 
Before 2000
70096 

Length

Max length11
Median length10
Mean length10.336843
Min length10

Characters and Unicode

Total characters2151066
Distinct characters10
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBefore 2000
2nd rowAfter 2000
3rd rowAfter 2000
4th rowBefore 2000
5th rowBefore 2000

Common Values

ValueCountFrequency (%)
After 2000 138001
27.1%
Before 2000 70096
 
13.8%
(Missing) 301502
59.2%

Length

2023-12-31T13:26:34.314082image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:34.502221image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
2000 208097
50.0%
after 138001
33.2%
before 70096
 
16.8%

Most occurring characters

ValueCountFrequency (%)
0 624291
29.0%
e 278193
12.9%
f 208097
 
9.7%
r 208097
 
9.7%
208097
 
9.7%
2 208097
 
9.7%
A 138001
 
6.4%
t 138001
 
6.4%
B 70096
 
3.3%
o 70096
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 902484
42.0%
Decimal Number 832388
38.7%
Space Separator 208097
 
9.7%
Uppercase Letter 208097
 
9.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 278193
30.8%
f 208097
23.1%
r 208097
23.1%
t 138001
15.3%
o 70096
 
7.8%
Decimal Number
ValueCountFrequency (%)
0 624291
75.0%
2 208097
 
25.0%
Uppercase Letter
ValueCountFrequency (%)
A 138001
66.3%
B 70096
33.7%
Space Separator
ValueCountFrequency (%)
208097
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1110581
51.6%
Common 1040485
48.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 278193
25.0%
f 208097
18.7%
r 208097
18.7%
A 138001
12.4%
t 138001
12.4%
B 70096
 
6.3%
o 70096
 
6.3%
Common
ValueCountFrequency (%)
0 624291
60.0%
208097
 
20.0%
2 208097
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2151066
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 624291
29.0%
e 278193
12.9%
f 208097
 
9.7%
r 208097
 
9.7%
208097
 
9.7%
2 208097
 
9.7%
A 138001
 
6.4%
t 138001
 
6.4%
B 70096
 
3.3%
o 70096
 
3.3%

has_hg_data
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
461636 
True
47963 
ValueCountFrequency (%)
False 461636
90.6%
True 47963
 
9.4%
2023-12-31T13:26:34.665293image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

hg_product_27
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49207 
1.0
 
1

Length

Max length4
Median length3
Mean length3.0965602
Min length3

Characters and Unicode

Total characters1578004
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49207
 
9.7%
1.0 1
 
< 0.1%

Length

2023-12-31T13:26:34.854564image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:35.029189image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49207
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49207
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49207
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1578004
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49207
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1578004
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49207
 
3.1%

hg_product_28
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
45570
1.0
 
3638

Length

Max length4
Median length3
Mean length3.0894233
Min length3

Characters and Unicode

Total characters1574367
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 45570
 
8.9%
1.0 3638
 
0.7%

Length

2023-12-31T13:26:35.216499image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:35.380938image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 45570
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.7%
Other Punctuation 509599
32.4%
Dash Punctuation 45570
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45570
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1574367
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 45570
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1574367
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 45570
 
2.9%

hg_product_29
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48364 
1.0
 
844

Length

Max length4
Median length3
Mean length3.094906
Min length3

Characters and Unicode

Total characters1577161
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48364
 
9.5%
1.0 844
 
0.2%

Length

2023-12-31T13:26:35.553833image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:35.710046image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48364
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48364
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48364
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577161
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48364
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577161
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48364
 
3.1%

hg_product_30
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48494 
1.0
 
714

Length

Max length4
Median length3
Mean length3.0951611
Min length3

Characters and Unicode

Total characters1577291
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48494
 
9.5%
1.0 714
 
0.1%

Length

2023-12-31T13:26:35.880680image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:36.039778image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48494
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48494
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48494
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577291
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48494
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577291
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48494
 
3.1%

hg_product_31
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
42277
1.0
 
6931

Length

Max length4
Median length3
Mean length3.0829613
Min length3

Characters and Unicode

Total characters1571074
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 42277
 
8.3%
1.0 6931
 
1.4%

Length

2023-12-31T13:26:36.209124image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:36.367238image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 42277
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.9%
Other Punctuation 509599
32.4%
Dash Punctuation 42277
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 42277
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1571074
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 42277
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1571074
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 42277
 
2.7%

hg_product_32
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
31188
1.0
 
18020

Length

Max length4
Median length3
Mean length3.0612011
Min length3

Characters and Unicode

Total characters1559985
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 31188
 
6.1%
1.0 18020
 
3.5%

Length

2023-12-31T13:26:36.542793image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:36.711717image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
62.2%
. 509599
32.7%
1 49208
 
3.2%
- 31188
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
65.3%
Other Punctuation 509599
32.7%
Dash Punctuation 31188
 
2.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 31188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1559985
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
62.2%
. 509599
32.7%
1 49208
 
3.2%
- 31188
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1559985
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
62.2%
. 509599
32.7%
1 49208
 
3.2%
- 31188
 
2.0%

hg_product_33
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
39219
1.0
 
9989

Length

Max length4
Median length3
Mean length3.0769605
Min length3

Characters and Unicode

Total characters1568016
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 39219
 
7.7%
1.0 9989
 
2.0%

Length

2023-12-31T13:26:36.894640image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:37.061785image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.9%
. 509599
32.5%
1 49208
 
3.1%
- 39219
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
65.0%
Other Punctuation 509599
32.5%
Dash Punctuation 39219
 
2.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 39219
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1568016
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.9%
. 509599
32.5%
1 49208
 
3.1%
- 39219
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1568016
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.9%
. 509599
32.5%
1 49208
 
3.1%
- 39219
 
2.5%

hg_product_34
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
38566
1.0
 
10642

Length

Max length4
Median length3
Mean length3.0756791
Min length3

Characters and Unicode

Total characters1567363
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 38566
 
7.6%
1.0 10642
 
2.1%

Length

2023-12-31T13:26:37.238442image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:37.394828image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.9%
. 509599
32.5%
1 49208
 
3.1%
- 38566
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
65.0%
Other Punctuation 509599
32.5%
Dash Punctuation 38566
 
2.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 38566
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1567363
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.9%
. 509599
32.5%
1 49208
 
3.1%
- 38566
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1567363
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.9%
. 509599
32.5%
1 49208
 
3.1%
- 38566
 
2.5%

hg_product_35
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48862 
1.0
 
346

Length

Max length4
Median length3
Mean length3.0958832
Min length3

Characters and Unicode

Total characters1577659
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48862
 
9.6%
1.0 346
 
0.1%

Length

2023-12-31T13:26:37.567005image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:37.724015image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48862
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48862
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48862
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577659
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48862
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577659
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48862
 
3.1%

hg_product_36
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
44725
1.0
 
4483

Length

Max length4
Median length3
Mean length3.0877651
Min length3

Characters and Unicode

Total characters1573522
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 44725
 
8.8%
1.0 4483
 
0.9%

Length

2023-12-31T13:26:37.900698image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:38.071405image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 44725
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.8%
Other Punctuation 509599
32.4%
Dash Punctuation 44725
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44725
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1573522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 44725
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1573522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 44725
 
2.8%

hg_product_37
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
46392 
1.0
 
2816

Length

Max length4
Median length3
Mean length3.0910363
Min length3

Characters and Unicode

Total characters1575189
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 46392
 
9.1%
1.0 2816
 
0.6%

Length

2023-12-31T13:26:38.257443image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:38.423085image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 46392
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.7%
Other Punctuation 509599
32.4%
Dash Punctuation 46392
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46392
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1575189
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 46392
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1575189
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 46392
 
2.9%

hg_product_38
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
42891
1.0
 
6317

Length

Max length4
Median length3
Mean length3.0841662
Min length3

Characters and Unicode

Total characters1571688
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 42891
 
8.4%
1.0 6317
 
1.2%

Length

2023-12-31T13:26:38.603917image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:38.760843image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 42891
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.8%
Other Punctuation 509599
32.4%
Dash Punctuation 42891
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 42891
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1571688
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 42891
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1571688
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 42891
 
2.7%

hg_product_39
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
44203
1.0
 
5005

Length

Max length4
Median length3
Mean length3.0867408
Min length3

Characters and Unicode

Total characters1573000
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 44203
 
8.7%
1.0 5005
 
1.0%

Length

2023-12-31T13:26:38.934702image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:39.091549image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 44203
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.8%
Other Punctuation 509599
32.4%
Dash Punctuation 44203
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44203
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1573000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 44203
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1573000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 44203
 
2.8%

hg_product_40
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49025 
1.0
 
183

Length

Max length4
Median length3
Mean length3.0962031
Min length3

Characters and Unicode

Total characters1577822
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49025
 
9.6%
1.0 183
 
< 0.1%

Length

2023-12-31T13:26:39.261147image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:39.419092image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49025
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49025
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49025
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577822
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49025
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577822
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49025
 
3.1%

hg_product_41
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
45407
1.0
 
3801

Length

Max length4
Median length3
Mean length3.0891034
Min length3

Characters and Unicode

Total characters1574204
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 45407
 
8.9%
1.0 3801
 
0.7%

Length

2023-12-31T13:26:39.588928image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:39.748316image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 45407
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.7%
Other Punctuation 509599
32.4%
Dash Punctuation 45407
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45407
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1574204
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 45407
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1574204
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 45407
 
2.9%

hg_product_42
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
45417
1.0
 
3791

Length

Max length4
Median length3
Mean length3.089123
Min length3

Characters and Unicode

Total characters1574214
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 45417
 
8.9%
1.0 3791
 
0.7%

Length

2023-12-31T13:26:39.919644image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:40.076826image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 45417
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.7%
Other Punctuation 509599
32.4%
Dash Punctuation 45417
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45417
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1574214
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 45417
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1574214
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 45417
 
2.9%

hg_product_43
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48492 
1.0
 
716

Length

Max length4
Median length3
Mean length3.0951572
Min length3

Characters and Unicode

Total characters1577289
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48492
 
9.5%
1.0 716
 
0.1%

Length

2023-12-31T13:26:40.249651image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:40.406135image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48492
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48492
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48492
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577289
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48492
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577289
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48492
 
3.1%

hg_product_44
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
44687
1.0
 
4521

Length

Max length4
Median length3
Mean length3.0876905
Min length3

Characters and Unicode

Total characters1573484
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 44687
 
8.8%
1.0 4521
 
0.9%

Length

2023-12-31T13:26:40.578572image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:40.734514image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 44687
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.8%
Other Punctuation 509599
32.4%
Dash Punctuation 44687
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44687
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1573484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 44687
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1573484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 44687
 
2.8%

hg_product_45
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
43092
1.0
 
6116

Length

Max length4
Median length3
Mean length3.0845606
Min length3

Characters and Unicode

Total characters1571889
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 43092
 
8.5%
1.0 6116
 
1.2%

Length

2023-12-31T13:26:40.904605image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:41.063400image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 43092
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.8%
Other Punctuation 509599
32.4%
Dash Punctuation 43092
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43092
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1571889
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 43092
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1571889
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 43092
 
2.7%

hg_product_46
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49111 
1.0
 
97

Length

Max length4
Median length3
Mean length3.0963719
Min length3

Characters and Unicode

Total characters1577908
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49111
 
9.6%
1.0 97
 
< 0.1%

Length

2023-12-31T13:26:41.232352image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:41.392436image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49111
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49111
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49111
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577908
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49111
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49111
 
3.1%

hg_product_47
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
33813
1.0
 
15395

Length

Max length4
Median length3
Mean length3.0663522
Min length3

Characters and Unicode

Total characters1562610
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 33813
 
6.6%
1.0 15395
 
3.0%

Length

2023-12-31T13:26:41.563090image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:41.716847image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
62.1%
. 509599
32.6%
1 49208
 
3.1%
- 33813
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
65.2%
Other Punctuation 509599
32.6%
Dash Punctuation 33813
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 33813
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1562610
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
62.1%
. 509599
32.6%
1 49208
 
3.1%
- 33813
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1562610
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
62.1%
. 509599
32.6%
1 49208
 
3.1%
- 33813
 
2.2%

hg_product_48
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
1.0
 
30357
-1.0
 
18851

Length

Max length4
Median length3
Mean length3.0369918
Min length3

Characters and Unicode

Total characters1547648
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
1.0 30357
 
6.0%
-1.0 18851
 
3.7%

Length

2023-12-31T13:26:41.887494image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:42.043264image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
62.7%
. 509599
32.9%
1 49208
 
3.2%
- 18851
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
65.9%
Other Punctuation 509599
32.9%
Dash Punctuation 18851
 
1.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18851
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1547648
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
62.7%
. 509599
32.9%
1 49208
 
3.2%
- 18851
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1547648
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
62.7%
. 509599
32.9%
1 49208
 
3.2%
- 18851
 
1.2%

hg_product_49
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
47500 
1.0
 
1708

Length

Max length4
Median length3
Mean length3.0932105
Min length3

Characters and Unicode

Total characters1576297
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 47500
 
9.3%
1.0 1708
 
0.3%

Length

2023-12-31T13:26:42.213104image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:42.373038image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 47500
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.7%
Other Punctuation 509599
32.3%
Dash Punctuation 47500
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1576297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 47500
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1576297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 47500
 
3.0%

hg_product_50
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
42682
1.0
 
6526

Length

Max length4
Median length3
Mean length3.0837561
Min length3

Characters and Unicode

Total characters1571479
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 42682
 
8.4%
1.0 6526
 
1.3%

Length

2023-12-31T13:26:42.554700image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:42.731543image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 42682
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.9%
Other Punctuation 509599
32.4%
Dash Punctuation 42682
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 42682
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1571479
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 42682
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1571479
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 42682
 
2.7%

hg_product_51
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
46945 
1.0
 
2263

Length

Max length4
Median length3
Mean length3.0921215
Min length3

Characters and Unicode

Total characters1575742
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 46945
 
9.2%
1.0 2263
 
0.4%

Length

2023-12-31T13:26:42.907454image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:43.065661image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.3%
1 49208
 
3.1%
- 46945
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.7%
Other Punctuation 509599
32.3%
Dash Punctuation 46945
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46945
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1575742
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.3%
1 49208
 
3.1%
- 46945
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1575742
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.3%
1 49208
 
3.1%
- 46945
 
3.0%

hg_product_52
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
47728 
1.0
 
1480

Length

Max length4
Median length3
Mean length3.093658
Min length3

Characters and Unicode

Total characters1576525
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 47728
 
9.4%
1.0 1480
 
0.3%

Length

2023-12-31T13:26:43.237833image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:43.394987image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 47728
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 47728
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47728
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1576525
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 47728
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1576525
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 47728
 
3.0%

hg_product_53
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48410 
1.0
 
798

Length

Max length4
Median length3
Mean length3.0949963
Min length3

Characters and Unicode

Total characters1577207
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48410
 
9.5%
1.0 798
 
0.2%

Length

2023-12-31T13:26:43.567852image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:43.739973image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48410
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48410
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48410
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48410
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48410
 
3.1%

hg_product_54
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49163 
1.0
 
45

Length

Max length4
Median length3
Mean length3.0964739
Min length3

Characters and Unicode

Total characters1577960
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49163
 
9.6%
1.0 45
 
< 0.1%

Length

2023-12-31T13:26:43.922599image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:44.083021image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49163
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49163
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49163
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577960
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49163
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49163
 
3.1%

hg_product_55
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
39493
1.0
 
9715

Length

Max length4
Median length3
Mean length3.0774982
Min length3

Characters and Unicode

Total characters1568290
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 39493
 
7.7%
1.0 9715
 
1.9%

Length

2023-12-31T13:26:44.278415image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:44.438132image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.9%
. 509599
32.5%
1 49208
 
3.1%
- 39493
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
65.0%
Other Punctuation 509599
32.5%
Dash Punctuation 39493
 
2.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 39493
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1568290
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.9%
. 509599
32.5%
1 49208
 
3.1%
- 39493
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1568290
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.9%
. 509599
32.5%
1 49208
 
3.1%
- 39493
 
2.5%

hg_product_56
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49135 
1.0
 
73

Length

Max length4
Median length3
Mean length3.0964189
Min length3

Characters and Unicode

Total characters1577932
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49135
 
9.6%
1.0 73
 
< 0.1%

Length

2023-12-31T13:26:44.608134image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:44.770404image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49135
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49135
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49135
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577932
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49135
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577932
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49135
 
3.1%

hg_product_57
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
45746
1.0
 
3462

Length

Max length4
Median length3
Mean length3.0897686
Min length3

Characters and Unicode

Total characters1574543
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 45746
 
9.0%
1.0 3462
 
0.7%

Length

2023-12-31T13:26:44.957855image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:45.127766image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 45746
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.7%
Other Punctuation 509599
32.4%
Dash Punctuation 45746
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45746
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1574543
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 45746
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1574543
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 45746
 
2.9%

hg_product_58
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49185 
1.0
 
23

Length

Max length4
Median length3
Mean length3.0965171
Min length3

Characters and Unicode

Total characters1577982
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49185
 
9.7%
1.0 23
 
< 0.1%

Length

2023-12-31T13:26:45.309160image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:45.470110image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49185
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49185
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49185
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577982
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49185
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577982
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49185
 
3.1%

hg_product_59
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49198 
1.0
 
10

Length

Max length4
Median length3
Mean length3.0965426
Min length3

Characters and Unicode

Total characters1577995
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49198
 
9.7%
1.0 10
 
< 0.1%

Length

2023-12-31T13:26:45.641175image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:45.806401image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49198
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49198
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49198
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577995
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49198
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577995
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49198
 
3.1%

hg_product_60
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48993 
1.0
 
215

Length

Max length4
Median length3
Mean length3.0961403
Min length3

Characters and Unicode

Total characters1577790
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48993
 
9.6%
1.0 215
 
< 0.1%

Length

2023-12-31T13:26:45.978412image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:46.138805image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48993
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48993
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48993
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577790
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48993
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577790
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48993
 
3.1%

hg_product_61
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49184 
1.0
 
24

Length

Max length4
Median length3
Mean length3.0965151
Min length3

Characters and Unicode

Total characters1577981
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49184
 
9.7%
1.0 24
 
< 0.1%

Length

2023-12-31T13:26:46.530916image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:46.689335image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49184
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49184
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49184
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577981
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49184
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577981
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49184
 
3.1%

hg_product_62
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
46363 
1.0
 
2845

Length

Max length4
Median length3
Mean length3.0909794
Min length3

Characters and Unicode

Total characters1575160
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 46363
 
9.1%
1.0 2845
 
0.6%

Length

2023-12-31T13:26:46.876047image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:47.032882image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 46363
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.7%
Other Punctuation 509599
32.4%
Dash Punctuation 46363
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46363
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1575160
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 46363
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1575160
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 46363
 
2.9%

hg_product_63
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
42557
1.0
 
6651

Length

Max length4
Median length3
Mean length3.0835108
Min length3

Characters and Unicode

Total characters1571354
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 42557
 
8.4%
1.0 6651
 
1.3%

Length

2023-12-31T13:26:47.205011image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:47.362788image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 42557
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.9%
Other Punctuation 509599
32.4%
Dash Punctuation 42557
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 42557
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1571354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 42557
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1571354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 42557
 
2.7%

hg_product_64
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49075 
1.0
 
133

Length

Max length4
Median length3
Mean length3.0963012
Min length3

Characters and Unicode

Total characters1577872
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49075
 
9.6%
1.0 133
 
< 0.1%

Length

2023-12-31T13:26:47.531668image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:47.689292image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49075
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49075
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49075
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577872
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49075
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577872
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49075
 
3.1%

hg_product_65
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
46567 
1.0
 
2641

Length

Max length4
Median length3
Mean length3.0913797
Min length3

Characters and Unicode

Total characters1575364
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 46567
 
9.1%
1.0 2641
 
0.5%

Length

2023-12-31T13:26:47.857658image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:48.014786image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.3%
1 49208
 
3.1%
- 46567
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.7%
Other Punctuation 509599
32.3%
Dash Punctuation 46567
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46567
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1575364
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.3%
1 49208
 
3.1%
- 46567
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1575364
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.3%
1 49208
 
3.1%
- 46567
 
3.0%

hg_product_66
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48608 
1.0
 
600

Length

Max length4
Median length3
Mean length3.0953848
Min length3

Characters and Unicode

Total characters1577405
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48608
 
9.5%
1.0 600
 
0.1%

Length

2023-12-31T13:26:48.184562image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:48.343296image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48608
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48608
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48608
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577405
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48608
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577405
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48608
 
3.1%

hg_product_67
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
28912
1.0
 
20296

Length

Max length4
Median length3
Mean length3.0567348
Min length3

Characters and Unicode

Total characters1557709
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 28912
 
5.7%
1.0 20296
 
4.0%

Length

2023-12-31T13:26:48.516214image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:48.675775image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
62.3%
. 509599
32.7%
1 49208
 
3.2%
- 28912
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
65.4%
Other Punctuation 509599
32.7%
Dash Punctuation 28912
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28912
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1557709
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
62.3%
. 509599
32.7%
1 49208
 
3.2%
- 28912
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1557709
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
62.3%
. 509599
32.7%
1 49208
 
3.2%
- 28912
 
1.9%

hg_product_68
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
44850
1.0
 
4358

Length

Max length4
Median length3
Mean length3.0880104
Min length3

Characters and Unicode

Total characters1573647
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 44850
 
8.8%
1.0 4358
 
0.9%

Length

2023-12-31T13:26:48.843778image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:49.001139image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 44850
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.8%
Other Punctuation 509599
32.4%
Dash Punctuation 44850
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44850
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1573647
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 44850
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1573647
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 44850
 
2.9%

hg_product_69
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
43499
1.0
 
5709

Length

Max length4
Median length3
Mean length3.0853593
Min length3

Characters and Unicode

Total characters1572296
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 43499
 
8.5%
1.0 5709
 
1.1%

Length

2023-12-31T13:26:49.173530image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:49.330544image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 43499
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.8%
Other Punctuation 509599
32.4%
Dash Punctuation 43499
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43499
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1572296
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 43499
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1572296
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 43499
 
2.8%

hg_product_70
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
47853 
1.0
 
1355

Length

Max length4
Median length3
Mean length3.0939032
Min length3

Characters and Unicode

Total characters1576650
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 47853
 
9.4%
1.0 1355
 
0.3%

Length

2023-12-31T13:26:49.500779image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:49.662469image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 47853
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 47853
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47853
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1576650
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 47853
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1576650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 47853
 
3.0%

hg_product_71
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48673 
1.0
 
535

Length

Max length4
Median length3
Mean length3.0955124
Min length3

Characters and Unicode

Total characters1577470
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48673
 
9.6%
1.0 535
 
0.1%

Length

2023-12-31T13:26:49.835730image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:49.996798image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48673
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48673
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48673
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48673
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48673
 
3.1%

hg_product_72
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
46079 
1.0
 
3129

Length

Max length4
Median length3
Mean length3.0904221
Min length3

Characters and Unicode

Total characters1574876
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 46079
 
9.0%
1.0 3129
 
0.6%

Length

2023-12-31T13:26:50.174345image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:50.334054image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 46079
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.7%
Other Punctuation 509599
32.4%
Dash Punctuation 46079
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46079
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1574876
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 46079
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1574876
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 46079
 
2.9%

hg_product_73
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
24817
1.0
 
24391

Length

Max length4
Median length3
Mean length3.0486991
Min length3

Characters and Unicode

Total characters1553614
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 24817
 
4.9%
1.0 24391
 
4.8%

Length

2023-12-31T13:26:50.511879image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:50.672503image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
62.4%
. 509599
32.8%
1 49208
 
3.2%
- 24817
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
65.6%
Other Punctuation 509599
32.8%
Dash Punctuation 24817
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24817
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1553614
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
62.4%
. 509599
32.8%
1 49208
 
3.2%
- 24817
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1553614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
62.4%
. 509599
32.8%
1 49208
 
3.2%
- 24817
 
1.6%

hg_product_74
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
46990 
1.0
 
2218

Length

Max length4
Median length3
Mean length3.0922098
Min length3

Characters and Unicode

Total characters1575787
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 46990
 
9.2%
1.0 2218
 
0.4%

Length

2023-12-31T13:26:50.845592image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:51.005806image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.3%
1 49208
 
3.1%
- 46990
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.7%
Other Punctuation 509599
32.3%
Dash Punctuation 46990
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46990
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1575787
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.3%
1 49208
 
3.1%
- 46990
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1575787
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.3%
1 49208
 
3.1%
- 46990
 
3.0%

hg_product_75
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
45800
1.0
 
3408

Length

Max length4
Median length3
Mean length3.0898746
Min length3

Characters and Unicode

Total characters1574597
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 45800
 
9.0%
1.0 3408
 
0.7%

Length

2023-12-31T13:26:51.214932image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:51.375402image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 45800
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.7%
Other Punctuation 509599
32.4%
Dash Punctuation 45800
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1574597
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 45800
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1574597
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 45800
 
2.9%

hg_product_76
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
43871
1.0
 
5337

Length

Max length4
Median length3
Mean length3.0860893
Min length3

Characters and Unicode

Total characters1572668
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 43871
 
8.6%
1.0 5337
 
1.0%

Length

2023-12-31T13:26:51.548076image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:51.712009image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 43871
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.8%
Other Punctuation 509599
32.4%
Dash Punctuation 43871
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43871
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1572668
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 43871
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1572668
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 43871
 
2.8%

hg_product_77
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
43707
1.0
 
5501

Length

Max length4
Median length3
Mean length3.0857674
Min length3

Characters and Unicode

Total characters1572504
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 43707
 
8.6%
1.0 5501
 
1.1%

Length

2023-12-31T13:26:51.886790image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:52.044172image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 43707
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.8%
Other Punctuation 509599
32.4%
Dash Punctuation 43707
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43707
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1572504
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 43707
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1572504
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 43707
 
2.8%

hg_product_78
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
47428 
1.0
 
1780

Length

Max length4
Median length3
Mean length3.0930693
Min length3

Characters and Unicode

Total characters1576225
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 47428
 
9.3%
1.0 1780
 
0.3%

Length

2023-12-31T13:26:52.218554image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:52.375480image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 47428
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.7%
Other Punctuation 509599
32.3%
Dash Punctuation 47428
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47428
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1576225
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 47428
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1576225
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 47428
 
3.0%

hg_product_79
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
45593
1.0
 
3615

Length

Max length4
Median length3
Mean length3.0894684
Min length3

Characters and Unicode

Total characters1574390
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 45593
 
8.9%
1.0 3615
 
0.7%

Length

2023-12-31T13:26:52.547639image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:52.702958image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 45593
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.7%
Other Punctuation 509599
32.4%
Dash Punctuation 45593
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45593
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1574390
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 45593
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1574390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 45593
 
2.9%

hg_product_80
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
47598 
1.0
 
1610

Length

Max length4
Median length3
Mean length3.0934029
Min length3

Characters and Unicode

Total characters1576395
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 47598
 
9.3%
1.0 1610
 
0.3%

Length

2023-12-31T13:26:52.872209image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:53.030617image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 47598
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.7%
Other Punctuation 509599
32.3%
Dash Punctuation 47598
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47598
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1576395
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 47598
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1576395
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 47598
 
3.0%

hg_product_81
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
46253 
1.0
 
2955

Length

Max length4
Median length3
Mean length3.0907635
Min length3

Characters and Unicode

Total characters1575050
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 46253
 
9.1%
1.0 2955
 
0.6%

Length

2023-12-31T13:26:53.201838image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:53.357428image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 46253
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.7%
Other Punctuation 509599
32.4%
Dash Punctuation 46253
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46253
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1575050
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 46253
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1575050
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 46253
 
2.9%

hg_product_82
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49201 
1.0
 
7

Length

Max length4
Median length3
Mean length3.0965485
Min length3

Characters and Unicode

Total characters1577998
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49201
 
9.7%
1.0 7
 
< 0.1%

Length

2023-12-31T13:26:53.530021image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:53.685746image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49201
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49201
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577998
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49201
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577998
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49201
 
3.1%

hg_product_83
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
36562
1.0
 
12646

Length

Max length4
Median length3
Mean length3.0717466
Min length3

Characters and Unicode

Total characters1565359
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 36562
 
7.2%
1.0 12646
 
2.5%

Length

2023-12-31T13:26:53.858360image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:54.014184image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
62.0%
. 509599
32.6%
1 49208
 
3.1%
- 36562
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
65.1%
Other Punctuation 509599
32.6%
Dash Punctuation 36562
 
2.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36562
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1565359
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
62.0%
. 509599
32.6%
1 49208
 
3.1%
- 36562
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1565359
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
62.0%
. 509599
32.6%
1 49208
 
3.1%
- 36562
 
2.3%

hg_product_84
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48477 
1.0
 
731

Length

Max length4
Median length3
Mean length3.0951277
Min length3

Characters and Unicode

Total characters1577274
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48477
 
9.5%
1.0 731
 
0.1%

Length

2023-12-31T13:26:54.180845image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:54.339619image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48477
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48477
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48477
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577274
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48477
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48477
 
3.1%

hg_product_85
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49198 
1.0
 
10

Length

Max length4
Median length3
Mean length3.0965426
Min length3

Characters and Unicode

Total characters1577995
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49198
 
9.7%
1.0 10
 
< 0.1%

Length

2023-12-31T13:26:54.510061image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:54.670849image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49198
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49198
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49198
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577995
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49198
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577995
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49198
 
3.1%

hg_product_86
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48538 
1.0
 
670

Length

Max length4
Median length3
Mean length3.0952474
Min length3

Characters and Unicode

Total characters1577335
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48538
 
9.5%
1.0 670
 
0.1%

Length

2023-12-31T13:26:54.841290image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:54.995137image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48538
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48538
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48538
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577335
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48538
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577335
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48538
 
3.1%

hg_product_87
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
30196
1.0
 
19012

Length

Max length4
Median length3
Mean length3.0592544
Min length3

Characters and Unicode

Total characters1558993
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 30196
 
5.9%
1.0 19012
 
3.7%

Length

2023-12-31T13:26:55.169678image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:55.326171image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
62.2%
. 509599
32.7%
1 49208
 
3.2%
- 30196
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
65.4%
Other Punctuation 509599
32.7%
Dash Punctuation 30196
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30196
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1558993
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
62.2%
. 509599
32.7%
1 49208
 
3.2%
- 30196
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1558993
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
62.2%
. 509599
32.7%
1 49208
 
3.2%
- 30196
 
1.9%

hg_product_88
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
44690
1.0
 
4518

Length

Max length4
Median length3
Mean length3.0876964
Min length3

Characters and Unicode

Total characters1573487
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 44690
 
8.8%
1.0 4518
 
0.9%

Length

2023-12-31T13:26:55.493847image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:55.649853image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 44690
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.8%
Other Punctuation 509599
32.4%
Dash Punctuation 44690
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44690
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1573487
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 44690
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1573487
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 44690
 
2.8%

hg_product_89
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48199 
1.0
 
1009

Length

Max length4
Median length3
Mean length3.0945822
Min length3

Characters and Unicode

Total characters1576996
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48199
 
9.5%
1.0 1009
 
0.2%

Length

2023-12-31T13:26:55.818635image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:55.976219image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48199
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48199
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48199
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1576996
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48199
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1576996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48199
 
3.1%

hg_product_90
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
47898 
1.0
 
1310

Length

Max length4
Median length3
Mean length3.0939916
Min length3

Characters and Unicode

Total characters1576695
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 47898
 
9.4%
1.0 1310
 
0.3%

Length

2023-12-31T13:26:56.146382image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:56.305552image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 47898
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 47898
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47898
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1576695
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 47898
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1576695
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 47898
 
3.0%

hg_product_91
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49193 
1.0
 
15

Length

Max length4
Median length3
Mean length3.0965328
Min length3

Characters and Unicode

Total characters1577990
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49193
 
9.7%
1.0 15
 
< 0.1%

Length

2023-12-31T13:26:56.475058image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:56.630623image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49193
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49193
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49193
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577990
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49193
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49193
 
3.1%

hg_product_92
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48762 
1.0
 
446

Length

Max length4
Median length3
Mean length3.095687
Min length3

Characters and Unicode

Total characters1577559
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48762
 
9.6%
1.0 446
 
0.1%

Length

2023-12-31T13:26:56.806834image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:56.963287image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48762
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48762
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48762
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577559
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48762
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577559
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48762
 
3.1%

hg_product_93
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49206 
1.0
 
2

Length

Max length4
Median length3
Mean length3.0965583
Min length3

Characters and Unicode

Total characters1578003
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49206
 
9.7%
1.0 2
 
< 0.1%

Length

2023-12-31T13:26:57.136053image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:57.292301image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49206
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49206
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49206
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1578003
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49206
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1578003
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49206
 
3.1%

hg_product_94
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49118 
1.0
 
90

Length

Max length4
Median length3
Mean length3.0963856
Min length3

Characters and Unicode

Total characters1577915
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49118
 
9.6%
1.0 90
 
< 0.1%

Length

2023-12-31T13:26:57.460729image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:57.619056image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49118
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49118
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49118
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577915
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49118
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577915
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49118
 
3.1%

hg_product_95
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
47338 
1.0
 
1870

Length

Max length4
Median length3
Mean length3.0928926
Min length3

Characters and Unicode

Total characters1576135
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 47338
 
9.3%
1.0 1870
 
0.4%

Length

2023-12-31T13:26:57.789629image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:57.946331image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 47338
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.7%
Other Punctuation 509599
32.3%
Dash Punctuation 47338
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47338
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1576135
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 47338
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1576135
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 47338
 
3.0%

hg_product_96
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
45617
1.0
 
3591

Length

Max length4
Median length3
Mean length3.0895155
Min length3

Characters and Unicode

Total characters1574414
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 45617
 
9.0%
1.0 3591
 
0.7%

Length

2023-12-31T13:26:58.119367image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:58.277343image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 45617
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.7%
Other Punctuation 509599
32.4%
Dash Punctuation 45617
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45617
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1574414
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 45617
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1574414
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 45617
 
2.9%

hg_product_97
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48938 
1.0
 
270

Length

Max length4
Median length3
Mean length3.0960324
Min length3

Characters and Unicode

Total characters1577735
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48938
 
9.6%
1.0 270
 
0.1%

Length

2023-12-31T13:26:58.448540image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:58.606441image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48938
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48938
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48938
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577735
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48938
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577735
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48938
 
3.1%

hg_product_98
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48708 
1.0
 
500

Length

Max length4
Median length3
Mean length3.095581
Min length3

Characters and Unicode

Total characters1577505
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48708
 
9.6%
1.0 500
 
0.1%

Length

2023-12-31T13:26:58.775093image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:58.934425image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48708
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48708
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48708
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577505
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48708
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577505
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48708
 
3.1%

hg_product_99
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48744 
1.0
 
464

Length

Max length4
Median length3
Mean length3.0956517
Min length3

Characters and Unicode

Total characters1577541
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48744
 
9.6%
1.0 464
 
0.1%

Length

2023-12-31T13:26:59.102649image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:59.259629image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48744
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48744
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48744
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577541
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48744
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577541
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48744
 
3.1%

hg_product_100
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
47981 
1.0
 
1227

Length

Max length4
Median length3
Mean length3.0941544
Min length3

Characters and Unicode

Total characters1576778
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 47981
 
9.4%
1.0 1227
 
0.2%

Length

2023-12-31T13:26:59.431149image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:59.585843image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 47981
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 47981
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47981
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1576778
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 47981
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1576778
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 47981
 
3.0%

hg_product_101
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48294 
1.0
 
914

Length

Max length4
Median length3
Mean length3.0947686
Min length3

Characters and Unicode

Total characters1577091
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48294
 
9.5%
1.0 914
 
0.2%

Length

2023-12-31T13:26:59.757551image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:26:59.915918image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48294
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48294
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48294
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577091
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48294
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577091
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48294
 
3.1%

hg_product_102
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
36680
1.0
 
12528

Length

Max length4
Median length3
Mean length3.0719782
Min length3

Characters and Unicode

Total characters1565477
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 36680
 
7.2%
1.0 12528
 
2.5%

Length

2023-12-31T13:27:00.091275image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:00.455407image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
62.0%
. 509599
32.6%
1 49208
 
3.1%
- 36680
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
65.1%
Other Punctuation 509599
32.6%
Dash Punctuation 36680
 
2.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36680
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1565477
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
62.0%
. 509599
32.6%
1 49208
 
3.1%
- 36680
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1565477
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
62.0%
. 509599
32.6%
1 49208
 
3.1%
- 36680
 
2.3%

hg_product_103
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
44973
1.0
 
4235

Length

Max length4
Median length3
Mean length3.0882517
Min length3

Characters and Unicode

Total characters1573770
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 44973
 
8.8%
1.0 4235
 
0.8%

Length

2023-12-31T13:27:00.621655image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:00.779137image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 44973
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.8%
Other Punctuation 509599
32.4%
Dash Punctuation 44973
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44973
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1573770
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 44973
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1573770
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 44973
 
2.9%

hg_product_104
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48596 
1.0
 
612

Length

Max length4
Median length3
Mean length3.0953613
Min length3

Characters and Unicode

Total characters1577393
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48596
 
9.5%
1.0 612
 
0.1%

Length

2023-12-31T13:27:00.948884image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:01.105922image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48596
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48596
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48596
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48596
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48596
 
3.1%

hg_product_105
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
46351 
1.0
 
2857

Length

Max length4
Median length3
Mean length3.0909558
Min length3

Characters and Unicode

Total characters1575148
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 46351
 
9.1%
1.0 2857
 
0.6%

Length

2023-12-31T13:27:01.276736image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:01.431347image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 46351
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.7%
Other Punctuation 509599
32.4%
Dash Punctuation 46351
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46351
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1575148
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 46351
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1575148
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 46351
 
2.9%

hg_product_106
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49013 
1.0
 
195

Length

Max length4
Median length3
Mean length3.0961795
Min length3

Characters and Unicode

Total characters1577810
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49013
 
9.6%
1.0 195
 
< 0.1%

Length

2023-12-31T13:27:01.604024image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:01.758209image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49013
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49013
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49013
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577810
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49013
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49013
 
3.1%

hg_product_107
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48955 
1.0
 
253

Length

Max length4
Median length3
Mean length3.0960657
Min length3

Characters and Unicode

Total characters1577752
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48955
 
9.6%
1.0 253
 
< 0.1%

Length

2023-12-31T13:27:01.935021image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:02.090396image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48955
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48955
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48955
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577752
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48955
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577752
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48955
 
3.1%

hg_product_108
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49204 
1.0
 
4

Length

Max length4
Median length3
Mean length3.0965543
Min length3

Characters and Unicode

Total characters1578001
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49204
 
9.7%
1.0 4
 
< 0.1%

Length

2023-12-31T13:27:02.261381image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:02.418217image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49204
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49204
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1578001
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49204
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1578001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49204
 
3.1%

hg_product_109
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49157 
1.0
 
51

Length

Max length4
Median length3
Mean length3.0964621
Min length3

Characters and Unicode

Total characters1577954
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49157
 
9.6%
1.0 51
 
< 0.1%

Length

2023-12-31T13:27:02.586749image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:02.744344image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49157
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49157
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49157
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577954
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49157
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577954
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49157
 
3.1%

hg_product_110
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49070 
1.0
 
138

Length

Max length4
Median length3
Mean length3.0962914
Min length3

Characters and Unicode

Total characters1577867
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49070
 
9.6%
1.0 138
 
< 0.1%

Length

2023-12-31T13:27:02.911996image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:03.069420image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49070
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49070
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49070
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577867
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49070
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577867
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49070
 
3.1%

hg_product_111
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49186 
1.0
 
22

Length

Max length4
Median length3
Mean length3.096519
Min length3

Characters and Unicode

Total characters1577983
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49186
 
9.7%
1.0 22
 
< 0.1%

Length

2023-12-31T13:27:03.236754image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:03.391849image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49186
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49186
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49186
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577983
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49186
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577983
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49186
 
3.1%

hg_product_112
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
47036 
1.0
 
2172

Length

Max length4
Median length3
Mean length3.0923
Min length3

Characters and Unicode

Total characters1575833
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 47036
 
9.2%
1.0 2172
 
0.4%

Length

2023-12-31T13:27:03.561610image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:03.717681image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.3%
1 49208
 
3.1%
- 47036
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.7%
Other Punctuation 509599
32.3%
Dash Punctuation 47036
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47036
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1575833
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.3%
1 49208
 
3.1%
- 47036
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1575833
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.3%
1 49208
 
3.1%
- 47036
 
3.0%

hg_product_113
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49192 
1.0
 
16

Length

Max length4
Median length3
Mean length3.0965308
Min length3

Characters and Unicode

Total characters1577989
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49192
 
9.7%
1.0 16
 
< 0.1%

Length

2023-12-31T13:27:03.889084image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:04.044081image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49192
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49192
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577989
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49192
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577989
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49192
 
3.1%

hg_product_114
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
47220 
1.0
 
1988

Length

Max length4
Median length3
Mean length3.0926611
Min length3

Characters and Unicode

Total characters1576017
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 47220
 
9.3%
1.0 1988
 
0.4%

Length

2023-12-31T13:27:04.212644image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:04.369182image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 47220
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.7%
Other Punctuation 509599
32.3%
Dash Punctuation 47220
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47220
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1576017
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 47220
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1576017
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 47220
 
3.0%

hg_product_115
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49133 
1.0
 
75

Length

Max length4
Median length3
Mean length3.096415
Min length3

Characters and Unicode

Total characters1577930
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49133
 
9.6%
1.0 75
 
< 0.1%

Length

2023-12-31T13:27:04.540417image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:04.720816image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49133
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49133
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49133
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49133
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49133
 
3.1%

hg_product_116
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48737 
1.0
 
471

Length

Max length4
Median length3
Mean length3.0956379
Min length3

Characters and Unicode

Total characters1577534
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48737
 
9.6%
1.0 471
 
0.1%

Length

2023-12-31T13:27:04.893053image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:05.048151image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48737
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48737
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48737
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577534
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48737
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577534
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48737
 
3.1%

hg_product_117
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49061 
1.0
 
147

Length

Max length4
Median length3
Mean length3.0962737
Min length3

Characters and Unicode

Total characters1577858
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49061
 
9.6%
1.0 147
 
< 0.1%

Length

2023-12-31T13:27:05.219391image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:05.375412image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49061
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49061
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49061
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49061
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49061
 
3.1%

hg_product_118
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49172 
1.0
 
36

Length

Max length4
Median length3
Mean length3.0964916
Min length3

Characters and Unicode

Total characters1577969
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49172
 
9.6%
1.0 36
 
< 0.1%

Length

2023-12-31T13:27:05.548185image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:05.705954image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49172
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49172
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49172
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577969
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49172
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577969
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49172
 
3.1%

hg_product_119
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
46197 
1.0
 
3011

Length

Max length4
Median length3
Mean length3.0906536
Min length3

Characters and Unicode

Total characters1574994
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 46197
 
9.1%
1.0 3011
 
0.6%

Length

2023-12-31T13:27:05.876504image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:06.037126image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 46197
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.7%
Other Punctuation 509599
32.4%
Dash Punctuation 46197
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46197
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1574994
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 46197
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1574994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 46197
 
2.9%

hg_product_120
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48767 
1.0
 
441

Length

Max length4
Median length3
Mean length3.0956968
Min length3

Characters and Unicode

Total characters1577564
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48767
 
9.6%
1.0 441
 
0.1%

Length

2023-12-31T13:27:06.204461image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:06.362342image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48767
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48767
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48767
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577564
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48767
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48767
 
3.1%

hg_product_121
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49133 
1.0
 
75

Length

Max length4
Median length3
Mean length3.096415
Min length3

Characters and Unicode

Total characters1577930
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49133
 
9.6%
1.0 75
 
< 0.1%

Length

2023-12-31T13:27:06.535791image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:06.693611image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49133
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49133
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49133
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49133
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49133
 
3.1%

hg_product_122
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49199 
1.0
 
9

Length

Max length4
Median length3
Mean length3.0965445
Min length3

Characters and Unicode

Total characters1577996
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49199
 
9.7%
1.0 9
 
< 0.1%

Length

2023-12-31T13:27:06.869479image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:07.027801image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49199
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49199
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49199
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577996
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49199
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49199
 
3.1%

hg_product_123
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48211 
1.0
 
997

Length

Max length4
Median length3
Mean length3.0946058
Min length3

Characters and Unicode

Total characters1577008
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48211
 
9.5%
1.0 997
 
0.2%

Length

2023-12-31T13:27:07.198873image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:07.354645image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48211
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48211
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48211
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577008
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48211
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48211
 
3.1%

hg_product_124
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
37948
1.0
 
11260

Length

Max length4
Median length3
Mean length3.0744664
Min length3

Characters and Unicode

Total characters1566745
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 37948
 
7.4%
1.0 11260
 
2.2%

Length

2023-12-31T13:27:07.526714image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:07.683806image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.9%
. 509599
32.5%
1 49208
 
3.1%
- 37948
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
65.1%
Other Punctuation 509599
32.5%
Dash Punctuation 37948
 
2.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 37948
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1566745
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.9%
. 509599
32.5%
1 49208
 
3.1%
- 37948
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1566745
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.9%
. 509599
32.5%
1 49208
 
3.1%
- 37948
 
2.4%

hg_product_125
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
34094
1.0
 
15114

Length

Max length4
Median length3
Mean length3.0669036
Min length3

Characters and Unicode

Total characters1562891
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 34094
 
6.7%
1.0 15114
 
3.0%

Length

2023-12-31T13:27:07.851743image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:08.006500image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
62.1%
. 509599
32.6%
1 49208
 
3.1%
- 34094
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
65.2%
Other Punctuation 509599
32.6%
Dash Punctuation 34094
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 34094
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1562891
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
62.1%
. 509599
32.6%
1 49208
 
3.1%
- 34094
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1562891
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
62.1%
. 509599
32.6%
1 49208
 
3.1%
- 34094
 
2.2%

hg_product_126
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48553 
1.0
 
655

Length

Max length4
Median length3
Mean length3.0952769
Min length3

Characters and Unicode

Total characters1577350
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48553
 
9.5%
1.0 655
 
0.1%

Length

2023-12-31T13:27:08.178785image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:08.334569image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48553
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48553
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48553
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577350
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48553
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577350
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48553
 
3.1%

hg_product_127
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48693 
1.0
 
515

Length

Max length4
Median length3
Mean length3.0955516
Min length3

Characters and Unicode

Total characters1577490
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48693
 
9.6%
1.0 515
 
0.1%

Length

2023-12-31T13:27:08.508156image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:08.695202image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48693
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48693
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48693
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577490
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48693
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577490
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48693
 
3.1%

hg_product_128
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49179 
1.0
 
29

Length

Max length4
Median length3
Mean length3.0965053
Min length3

Characters and Unicode

Total characters1577976
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49179
 
9.7%
1.0 29
 
< 0.1%

Length

2023-12-31T13:27:08.870710image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:09.037954image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49179
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49179
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577976
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49179
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49179
 
3.1%

hg_product_129
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
28622
1.0
 
20586

Length

Max length4
Median length3
Mean length3.0561657
Min length3

Characters and Unicode

Total characters1557419
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 28622
 
5.6%
1.0 20586
 
4.0%

Length

2023-12-31T13:27:09.220250image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:09.387299image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
62.3%
. 509599
32.7%
1 49208
 
3.2%
- 28622
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
65.4%
Other Punctuation 509599
32.7%
Dash Punctuation 28622
 
1.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28622
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1557419
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
62.3%
. 509599
32.7%
1 49208
 
3.2%
- 28622
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1557419
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
62.3%
. 509599
32.7%
1 49208
 
3.2%
- 28622
 
1.8%

hg_product_130
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49060 
1.0
 
148

Length

Max length4
Median length3
Mean length3.0962718
Min length3

Characters and Unicode

Total characters1577857
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49060
 
9.6%
1.0 148
 
< 0.1%

Length

2023-12-31T13:27:09.562193image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:09.722252image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49060
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49060
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49060
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577857
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49060
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577857
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49060
 
3.1%

hg_product_131
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48977 
1.0
 
231

Length

Max length4
Median length3
Mean length3.0961089
Min length3

Characters and Unicode

Total characters1577774
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48977
 
9.6%
1.0 231
 
< 0.1%

Length

2023-12-31T13:27:09.954424image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:10.124723image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48977
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48977
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48977
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577774
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48977
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577774
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48977
 
3.1%

hg_product_132
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
43839
1.0
 
5369

Length

Max length4
Median length3
Mean length3.0860265
Min length3

Characters and Unicode

Total characters1572636
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 43839
 
8.6%
1.0 5369
 
1.1%

Length

2023-12-31T13:27:10.304427image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:10.462277image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 43839
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.8%
Other Punctuation 509599
32.4%
Dash Punctuation 43839
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43839
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1572636
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 43839
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1572636
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 43839
 
2.8%

hg_product_133
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49013 
1.0
 
195

Length

Max length4
Median length3
Mean length3.0961795
Min length3

Characters and Unicode

Total characters1577810
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49013
 
9.6%
1.0 195
 
< 0.1%

Length

2023-12-31T13:27:10.634665image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:10.795870image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49013
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49013
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49013
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577810
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49013
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49013
 
3.1%

hg_product_134
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49079 
1.0
 
129

Length

Max length4
Median length3
Mean length3.0963091
Min length3

Characters and Unicode

Total characters1577876
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49079
 
9.6%
1.0 129
 
< 0.1%

Length

2023-12-31T13:27:10.972883image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:11.137039image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49079
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49079
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49079
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577876
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49079
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577876
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49079
 
3.1%

hg_product_135
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49195 
1.0
 
13

Length

Max length4
Median length3
Mean length3.0965367
Min length3

Characters and Unicode

Total characters1577992
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49195
 
9.7%
1.0 13
 
< 0.1%

Length

2023-12-31T13:27:11.312132image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:11.473729image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49195
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49195
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49195
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577992
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49195
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577992
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49195
 
3.1%

hg_product_136
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49184 
1.0
 
24

Length

Max length4
Median length3
Mean length3.0965151
Min length3

Characters and Unicode

Total characters1577981
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49184
 
9.7%
1.0 24
 
< 0.1%

Length

2023-12-31T13:27:11.698887image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:11.869255image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49184
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49184
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49184
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577981
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49184
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577981
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49184
 
3.1%

hg_product_137
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48735 
1.0
 
473

Length

Max length4
Median length3
Mean length3.095634
Min length3

Characters and Unicode

Total characters1577532
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48735
 
9.6%
1.0 473
 
0.1%

Length

2023-12-31T13:27:12.049372image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:12.209052image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48735
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48735
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48735
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577532
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48735
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577532
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48735
 
3.1%

hg_product_138
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48956 
1.0
 
252

Length

Max length4
Median length3
Mean length3.0960677
Min length3

Characters and Unicode

Total characters1577753
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48956
 
9.6%
1.0 252
 
< 0.1%

Length

2023-12-31T13:27:12.381359image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:12.544945image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48956
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48956
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48956
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577753
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48956
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577753
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48956
 
3.1%

hg_product_139
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
45354
1.0
 
3854

Length

Max length4
Median length3
Mean length3.0889994
Min length3

Characters and Unicode

Total characters1574151
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 45354
 
8.9%
1.0 3854
 
0.8%

Length

2023-12-31T13:27:12.720749image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:12.889750image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 45354
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.7%
Other Punctuation 509599
32.4%
Dash Punctuation 45354
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45354
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1574151
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 45354
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1574151
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 45354
 
2.9%

hg_product_140
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
41590
1.0
 
7618

Length

Max length4
Median length3
Mean length3.0816132
Min length3

Characters and Unicode

Total characters1570387
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 41590
 
8.2%
1.0 7618
 
1.5%

Length

2023-12-31T13:27:13.081737image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:13.254125image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.8%
. 509599
32.5%
1 49208
 
3.1%
- 41590
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.9%
Other Punctuation 509599
32.5%
Dash Punctuation 41590
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 41590
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1570387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.8%
. 509599
32.5%
1 49208
 
3.1%
- 41590
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1570387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.8%
. 509599
32.5%
1 49208
 
3.1%
- 41590
 
2.6%

hg_product_141
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48263 
1.0
 
945

Length

Max length4
Median length3
Mean length3.0947078
Min length3

Characters and Unicode

Total characters1577060
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48263
 
9.5%
1.0 945
 
0.2%

Length

2023-12-31T13:27:13.434849image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:13.606568image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48263
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48263
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48263
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577060
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48263
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48263
 
3.1%

hg_product_142
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48721 
1.0
 
487

Length

Max length4
Median length3
Mean length3.0956065
Min length3

Characters and Unicode

Total characters1577518
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48721
 
9.6%
1.0 487
 
0.1%

Length

2023-12-31T13:27:13.793468image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:13.964713image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48721
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48721
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48721
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577518
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48721
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577518
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48721
 
3.1%

hg_product_143
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
44799
1.0
 
4409

Length

Max length4
Median length3
Mean length3.0879103
Min length3

Characters and Unicode

Total characters1573596
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 44799
 
8.8%
1.0 4409
 
0.9%

Length

2023-12-31T13:27:14.138191image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:14.298792image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 44799
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.8%
Other Punctuation 509599
32.4%
Dash Punctuation 44799
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44799
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1573596
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 44799
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1573596
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.6%
. 509599
32.4%
1 49208
 
3.1%
- 44799
 
2.8%

hg_product_144
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
34538
1.0
 
14670

Length

Max length4
Median length3
Mean length3.0677749
Min length3

Characters and Unicode

Total characters1563335
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 34538
 
6.8%
1.0 14670
 
2.9%

Length

2023-12-31T13:27:14.485350image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:14.656796image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
62.0%
. 509599
32.6%
1 49208
 
3.1%
- 34538
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
65.2%
Other Punctuation 509599
32.6%
Dash Punctuation 34538
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 34538
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1563335
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
62.0%
. 509599
32.6%
1 49208
 
3.1%
- 34538
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1563335
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
62.0%
. 509599
32.6%
1 49208
 
3.1%
- 34538
 
2.2%

hg_product_145
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
 
42547
1.0
 
6661

Length

Max length4
Median length3
Mean length3.0834911
Min length3

Characters and Unicode

Total characters1571344
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 42547
 
8.3%
1.0 6661
 
1.3%

Length

2023-12-31T13:27:15.066406image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:15.228521image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 42547
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.9%
Other Punctuation 509599
32.4%
Dash Punctuation 42547
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 42547
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1571344
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 42547
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1571344
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.7%
. 509599
32.4%
1 49208
 
3.1%
- 42547
 
2.7%

hg_product_146
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48994 
1.0
 
214

Length

Max length4
Median length3
Mean length3.0961423
Min length3

Characters and Unicode

Total characters1577791
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48994
 
9.6%
1.0 214
 
< 0.1%

Length

2023-12-31T13:27:15.421285image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:15.599240image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48994
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48994
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48994
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577791
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48994
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577791
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48994
 
3.1%

hg_product_147
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
48832 
1.0
 
376

Length

Max length4
Median length3
Mean length3.0958244
Min length3

Characters and Unicode

Total characters1577629
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 48832
 
9.6%
1.0 376
 
0.1%

Length

2023-12-31T13:27:15.792671image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:15.954161image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48832
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 48832
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48832
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577629
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48832
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577629
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 48832
 
3.1%

hg_product_148
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
0.0
460391 
-1.0
49159 
1.0
 
49

Length

Max length4
Median length3
Mean length3.096466
Min length3

Characters and Unicode

Total characters1577956
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460391
90.3%
-1.0 49159
 
9.6%
1.0 49
 
< 0.1%

Length

2023-12-31T13:27:16.126903image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T13:27:16.349964image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460391
90.3%
1.0 49208
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49159
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1019198
64.6%
Other Punctuation 509599
32.3%
Dash Punctuation 49159
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 969990
95.2%
1 49208
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 509599
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49159
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1577956
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49159
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1577956
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 969990
61.5%
. 509599
32.3%
1 49208
 
3.1%
- 49159
 
3.1%

country_top
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.2 MiB
US
192674 
Other
115037 
GB
44373 
Missing
39738 
IN
32745 
Other values (6)
85032 

Length

Max length7
Median length2
Mean length3.0671155
Min length2

Characters and Unicode

Total characters1562999
Distinct characters23
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowNL
3rd rowMissing
4th rowOther
5th rowIN

Common Values

ValueCountFrequency (%)
US 192674
37.8%
Other 115037
22.6%
GB 44373
 
8.7%
Missing 39738
 
7.8%
IN 32745
 
6.4%
DE 20320
 
4.0%
CA 16384
 
3.2%
FR 16160
 
3.2%
AU 15380
 
3.0%
NL 9280
 
1.8%

Length

2023-12-31T13:27:16.574371image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
us 192674
37.8%
other 115037
22.6%
gb 44373
 
8.7%
missing 39738
 
7.8%
in 32745
 
6.4%
de 20320
 
4.0%
ca 16384
 
3.2%
fr 16160
 
3.2%
au 15380
 
3.0%
nl 9280
 
1.8%

Most occurring characters

ValueCountFrequency (%)
U 208054
13.3%
S 192674
12.3%
O 115037
 
7.4%
t 115037
 
7.4%
h 115037
 
7.4%
e 115037
 
7.4%
r 115037
 
7.4%
i 79476
 
5.1%
s 79476
 
5.1%
B 51881
 
3.3%
Other values (13) 376253
24.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 864423
55.3%
Lowercase Letter 698576
44.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 208054
24.1%
S 192674
22.3%
O 115037
13.3%
B 51881
 
6.0%
G 44373
 
5.1%
N 42025
 
4.9%
M 39738
 
4.6%
I 32745
 
3.8%
A 31764
 
3.7%
R 23668
 
2.7%
Other values (5) 82464
 
9.5%
Lowercase Letter
ValueCountFrequency (%)
t 115037
16.5%
h 115037
16.5%
e 115037
16.5%
r 115037
16.5%
i 79476
11.4%
s 79476
11.4%
n 39738
 
5.7%
g 39738
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 1562999
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 208054
13.3%
S 192674
12.3%
O 115037
 
7.4%
t 115037
 
7.4%
h 115037
 
7.4%
e 115037
 
7.4%
r 115037
 
7.4%
i 79476
 
5.1%
s 79476
 
5.1%
B 51881
 
3.3%
Other values (13) 376253
24.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1562999
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 208054
13.3%
S 192674
12.3%
O 115037
 
7.4%
t 115037
 
7.4%
h 115037
 
7.4%
e 115037
 
7.4%
r 115037
 
7.4%
i 79476
 
5.1%
s 79476
 
5.1%
B 51881
 
3.3%
Other values (13) 376253
24.1%

state_top
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size30.4 MiB
Missing
319941 
Other
71032 
CA
41553 
NY
 
20829
TX
 
13310
Other values (6)
42934 

Length

Max length7
Median length7
Mean length5.5573088
Min length2

Characters and Unicode

Total characters2831999
Distinct characters21
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMissing
2nd rowMissing
3rd rowMissing
4th rowMissing
5th rowMissing

Common Values

ValueCountFrequency (%)
Missing 319941
62.8%
Other 71032
 
13.9%
CA 41553
 
8.2%
NY 20829
 
4.1%
TX 13310
 
2.6%
FL 10321
 
2.0%
IL 7908
 
1.6%
MA 7304
 
1.4%
WA 6142
 
1.2%
CO 5923
 
1.2%

Length

2023-12-31T13:27:16.841272image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
missing 319941
62.8%
other 71032
 
13.9%
ca 41553
 
8.2%
ny 20829
 
4.1%
tx 13310
 
2.6%
fl 10321
 
2.0%
il 7908
 
1.6%
ma 7304
 
1.4%
wa 6142
 
1.2%
co 5923
 
1.2%

Most occurring characters

ValueCountFrequency (%)
s 639882
22.6%
i 639882
22.6%
M 327245
11.6%
n 319941
11.3%
g 319941
11.3%
O 76955
 
2.7%
t 71032
 
2.5%
h 71032
 
2.5%
e 71032
 
2.5%
r 71032
 
2.5%
Other values (11) 224025
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2203774
77.8%
Uppercase Letter 628225
 
22.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 327245
52.1%
O 76955
 
12.2%
A 60335
 
9.6%
C 47476
 
7.6%
N 20829
 
3.3%
Y 20829
 
3.3%
L 18229
 
2.9%
T 13310
 
2.1%
X 13310
 
2.1%
F 10321
 
1.6%
Other values (3) 19386
 
3.1%
Lowercase Letter
ValueCountFrequency (%)
s 639882
29.0%
i 639882
29.0%
n 319941
14.5%
g 319941
14.5%
t 71032
 
3.2%
h 71032
 
3.2%
e 71032
 
3.2%
r 71032
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 2831999
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 639882
22.6%
i 639882
22.6%
M 327245
11.6%
n 319941
11.3%
g 319941
11.3%
O 76955
 
2.7%
t 71032
 
2.5%
h 71032
 
2.5%
e 71032
 
2.5%
r 71032
 
2.5%
Other values (11) 224025
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2831999
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 639882
22.6%
i 639882
22.6%
M 327245
11.6%
n 319941
11.3%
g 319941
11.3%
O 76955
 
2.7%
t 71032
 
2.5%
h 71032
 
2.5%
e 71032
 
2.5%
r 71032
 
2.5%
Other values (11) 224025
 
7.9%

country_top_AU
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
494219 
True
 
15380
ValueCountFrequency (%)
False 494219
97.0%
True 15380
 
3.0%
2023-12-31T13:27:17.001757image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_BR
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
502091 
True
 
7508
ValueCountFrequency (%)
False 502091
98.5%
True 7508
 
1.5%
2023-12-31T13:27:17.147940image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_CA
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
493215 
True
 
16384
ValueCountFrequency (%)
False 493215
96.8%
True 16384
 
3.2%
2023-12-31T13:27:17.305680image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_DE
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
489279 
True
 
20320
ValueCountFrequency (%)
False 489279
96.0%
True 20320
 
4.0%
2023-12-31T13:27:17.454385image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_FR
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
493439 
True
 
16160
ValueCountFrequency (%)
False 493439
96.8%
True 16160
 
3.2%
2023-12-31T13:27:17.602734image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_GB
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
465226 
True
 
44373
ValueCountFrequency (%)
False 465226
91.3%
True 44373
 
8.7%
2023-12-31T13:27:17.737135image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_IN
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
476854 
True
 
32745
ValueCountFrequency (%)
False 476854
93.6%
True 32745
 
6.4%
2023-12-31T13:27:17.873130image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_Missing
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
469861 
True
 
39738
ValueCountFrequency (%)
False 469861
92.2%
True 39738
 
7.8%
2023-12-31T13:27:18.012461image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_NL
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
500319 
True
 
9280
ValueCountFrequency (%)
False 500319
98.2%
True 9280
 
1.8%
2023-12-31T13:27:18.144777image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_Other
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
394562 
True
115037 
ValueCountFrequency (%)
False 394562
77.4%
True 115037
 
22.6%
2023-12-31T13:27:18.277749image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

country_top_US
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
316925 
True
192674 
ValueCountFrequency (%)
False 316925
62.2%
True 192674
37.8%
2023-12-31T13:27:18.420034image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_CA
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
468046 
True
 
41553
ValueCountFrequency (%)
False 468046
91.8%
True 41553
 
8.2%
2023-12-31T13:27:18.566636image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_CO
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
503676 
True
 
5923
ValueCountFrequency (%)
False 503676
98.8%
True 5923
 
1.2%
2023-12-31T13:27:18.712573image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_FL
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
499278 
True
 
10321
ValueCountFrequency (%)
False 499278
98.0%
True 10321
 
2.0%
2023-12-31T13:27:18.851002image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_GA
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
504263 
True
 
5336
ValueCountFrequency (%)
False 504263
99.0%
True 5336
 
1.0%
2023-12-31T13:27:18.985992image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_IL
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
501691 
True
 
7908
ValueCountFrequency (%)
False 501691
98.4%
True 7908
 
1.6%
2023-12-31T13:27:19.121545image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_MA
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
502295 
True
 
7304
ValueCountFrequency (%)
False 502295
98.6%
True 7304
 
1.4%
2023-12-31T13:27:19.252622image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_Missing
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
True
319941 
False
189658 
ValueCountFrequency (%)
True 319941
62.8%
False 189658
37.2%
2023-12-31T13:27:19.386077image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_NY
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
488770 
True
 
20829
ValueCountFrequency (%)
False 488770
95.9%
True 20829
 
4.1%
2023-12-31T13:27:19.527100image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_Other
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
438567 
True
71032 
ValueCountFrequency (%)
False 438567
86.1%
True 71032
 
13.9%
2023-12-31T13:27:19.661413image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_TX
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
496289 
True
 
13310
ValueCountFrequency (%)
False 496289
97.4%
True 13310
 
2.6%
2023-12-31T13:27:19.800498image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

state_top_WA
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
503457 
True
 
6142
ValueCountFrequency (%)
False 503457
98.8%
True 6142
 
1.2%
2023-12-31T13:27:19.937685image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
449703 
True
59896 
ValueCountFrequency (%)
False 449703
88.2%
True 59896
 
11.8%
2023-12-31T13:27:20.071590image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

industry_2. Tech - Software Publisher
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
503003 
True
 
6596
ValueCountFrequency (%)
False 503003
98.7%
True 6596
 
1.3%
2023-12-31T13:27:20.207897image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

industry_3. Finance
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
481602 
True
 
27997
ValueCountFrequency (%)
False 481602
94.5%
True 27997
 
5.5%
2023-12-31T13:27:20.346594image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

industry_4. Consulting
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
490282 
True
 
19317
ValueCountFrequency (%)
False 490282
96.2%
True 19317
 
3.8%
2023-12-31T13:27:20.482607image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

industry_5. Retail
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
485831 
True
 
23768
ValueCountFrequency (%)
False 485831
95.3%
True 23768
 
4.7%
2023-12-31T13:27:20.620866image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

industry_6. Manufacturing
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
470497 
True
 
39102
ValueCountFrequency (%)
False 470497
92.3%
True 39102
 
7.7%
2023-12-31T13:27:20.757563image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

industry_7. Wholesale Trade
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
489281 
True
 
20318
ValueCountFrequency (%)
False 489281
96.0%
True 20318
 
4.0%
2023-12-31T13:27:20.894171image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

industry_8. Healthcare
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
494450 
True
 
15149
ValueCountFrequency (%)
False 494450
97.0%
True 15149
 
3.0%
2023-12-31T13:27:21.035119image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

industry_9. Other
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
281803 
True
227796 
ValueCountFrequency (%)
False 281803
55.3%
True 227796
44.7%
2023-12-31T13:27:21.171703image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

industry_nan
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
439939 
True
69660 
ValueCountFrequency (%)
False 439939
86.3%
True 69660
 
13.7%
2023-12-31T13:27:21.325651image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

revenue_Over 10B
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
507066 
True
 
2533
ValueCountFrequency (%)
False 507066
99.5%
True 2533
 
0.5%
2023-12-31T13:27:21.479305image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

revenue_Under 100M
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
500187 
True
 
9412
ValueCountFrequency (%)
False 500187
98.2%
True 9412
 
1.8%
2023-12-31T13:27:21.618485image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

revenue_Under 10B
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
501296 
True
 
8303
ValueCountFrequency (%)
False 501296
98.4%
True 8303
 
1.6%
2023-12-31T13:27:21.752854image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

revenue_Under 10M
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
507886 
True
 
1713
ValueCountFrequency (%)
False 507886
99.7%
True 1713
 
0.3%
2023-12-31T13:27:21.890341image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

revenue_Under 1B
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
490660 
True
 
18939
ValueCountFrequency (%)
False 490660
96.3%
True 18939
 
3.7%
2023-12-31T13:27:22.035244image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

revenue_Under 1M
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
508822 
True
 
777
ValueCountFrequency (%)
False 508822
99.8%
True 777
 
0.2%
2023-12-31T13:27:22.192398image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

revenue_nan
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
True
467922 
False
 
41677
ValueCountFrequency (%)
True 467922
91.8%
False 41677
 
8.2%
2023-12-31T13:27:22.334407image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

founded_After 2000
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
371598 
True
138001 
ValueCountFrequency (%)
False 371598
72.9%
True 138001
 
27.1%
2023-12-31T13:27:22.468803image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

founded_Before 2000
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
439503 
True
70096 
ValueCountFrequency (%)
False 439503
86.2%
True 70096
 
13.8%
2023-12-31T13:27:22.622015image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

founded_nan
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
True
301502 
False
208097 
ValueCountFrequency (%)
True 301502
59.2%
False 208097
40.8%
2023-12-31T13:27:22.772545image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Correlations

2023-12-31T13:27:23.323863image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
company_revenue_bucketcountry_topcountry_top_AUcountry_top_BRcountry_top_CAcountry_top_DEcountry_top_FRcountry_top_GBcountry_top_INcountry_top_Missingcountry_top_NLcountry_top_Othercountry_top_UScrossbeam_product10_customercrossbeam_product11_customercrossbeam_product12_customercrossbeam_product13_customercrossbeam_product14_customercrossbeam_product15_customercrossbeam_product16_customercrossbeam_product17_customercrossbeam_product18_customercrossbeam_product19_customercrossbeam_product1_customercrossbeam_product20_customercrossbeam_product21_customercrossbeam_product22_customercrossbeam_product23_customercrossbeam_product2_customercrossbeam_product3_customercrossbeam_product4_customercrossbeam_product5_customercrossbeam_product6_customercrossbeam_product7_customercrossbeam_product8_customercrossbeam_product9_customerdnb_founded_time_groupedfounded_After 2000founded_Before 2000founded_nanhas_crossbeam_datahas_hg_datahg_product_100hg_product_101hg_product_102hg_product_103hg_product_104hg_product_105hg_product_106hg_product_107hg_product_108hg_product_109hg_product_110hg_product_111hg_product_112hg_product_113hg_product_114hg_product_115hg_product_116hg_product_117hg_product_118hg_product_119hg_product_120hg_product_121hg_product_122hg_product_123hg_product_124hg_product_125hg_product_126hg_product_127hg_product_128hg_product_129hg_product_130hg_product_131hg_product_132hg_product_133hg_product_134hg_product_135hg_product_136hg_product_137hg_product_138hg_product_139hg_product_140hg_product_141hg_product_142hg_product_143hg_product_144hg_product_145hg_product_146hg_product_147hg_product_148hg_product_27hg_product_28hg_product_29hg_product_30hg_product_31hg_product_32hg_product_33hg_product_34hg_product_35hg_product_36hg_product_37hg_product_38hg_product_39hg_product_40hg_product_41hg_product_42hg_product_43hg_product_44hg_product_45hg_product_46hg_product_47hg_product_48hg_product_49hg_product_50hg_product_51hg_product_52hg_product_53hg_product_54hg_product_55hg_product_56hg_product_57hg_product_58hg_product_59hg_product_60hg_product_61hg_product_62hg_product_63hg_product_64hg_product_65hg_product_66hg_product_67hg_product_68hg_product_69hg_product_70hg_product_71hg_product_72hg_product_73hg_product_74hg_product_75hg_product_76hg_product_77hg_product_78hg_product_79hg_product_80hg_product_81hg_product_82hg_product_83hg_product_84hg_product_85hg_product_86hg_product_87hg_product_88hg_product_89hg_product_90hg_product_91hg_product_92hg_product_93hg_product_94hg_product_95hg_product_96hg_product_97hg_product_98hg_product_99industry_1. Tech - Computer systems design and related servicesindustry_2. Tech - Software Publisherindustry_3. Financeindustry_4. Consultingindustry_5. Retailindustry_6. Manufacturingindustry_7. Wholesale Tradeindustry_8. Healthcareindustry_9. Otherindustry_groupedindustry_nanis_arr_over_12kis_current_customeris_self_servicerevenue_Over 10Brevenue_Under 100Mrevenue_Under 10Brevenue_Under 10Mrevenue_Under 1Brevenue_Under 1Mrevenue_nanstatestate_topstate_top_CAstate_top_COstate_top_FLstate_top_GAstate_top_ILstate_top_MAstate_top_Missingstate_top_NYstate_top_Otherstate_top_TXstate_top_WA
company_revenue_bucket1.0000.0540.0230.0230.0000.0330.0300.0610.0690.0150.0270.0420.0520.0960.0950.1030.0970.1130.0980.0950.1060.0970.1340.1020.1260.1080.1170.0990.0950.0950.0950.1350.1400.1220.0990.1060.1050.0630.0820.0480.1290.2550.1910.1870.2590.2260.1760.2170.1600.1600.1520.1530.1600.1520.2160.1520.2070.1560.1660.1580.1530.2200.1640.1550.1520.1540.2490.2280.1780.1710.1520.2130.1590.1630.2540.1660.1560.1530.1520.1710.1610.2060.2210.1800.1600.2380.2530.1940.1540.1560.1510.1510.2000.1720.1800.2190.2130.2540.2450.1620.2180.2150.2330.2380.1580.2310.2210.1700.2220.2390.1590.2110.2190.1860.2190.2000.1700.1790.1520.1920.1530.1770.1510.1510.1550.1520.2170.2460.1570.2230.1580.2280.2040.1920.1770.1590.1850.2290.1920.2010.2190.2210.1910.2110.1900.2070.1510.2330.1740.1520.1690.2240.1790.1610.1620.1520.1720.1520.1550.2090.2280.1610.1730.1710.1210.0360.0630.0470.0360.0840.0170.0550.0560.0820.0530.0240.0330.0281.0001.0001.0001.0001.0001.0001.0000.0470.0320.0310.0000.0000.0120.0150.0000.0510.0170.0380.0210.033
country_top0.0541.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.1750.1750.1770.1750.1760.1750.1750.1750.1760.1760.1750.1760.1750.1750.1770.1750.1750.1750.1750.1770.1770.1750.1750.2430.3730.1560.4080.2470.1130.0820.0820.0870.0850.0810.0840.0800.0810.0800.0810.0800.0800.0830.0800.0830.0800.0810.0810.0800.0830.0810.0800.0800.0860.0880.0890.0820.0810.0800.1070.0820.0810.0860.0800.0800.0800.0800.0820.0810.0910.0960.0820.0820.0880.0870.0930.0810.0810.0800.0800.0870.0820.0820.0930.0910.0870.0850.0810.0870.0860.0880.0860.0800.0840.0880.0820.0860.0870.0800.0870.0900.0810.0840.0830.0830.0810.0800.0970.0810.0890.0800.0800.0800.0800.0830.0860.0800.0840.0830.0920.0850.0970.0830.0810.0860.0970.0850.0870.0890.0890.0840.0870.0830.0850.0800.0880.0820.0800.0810.0900.0960.0840.0850.0800.0810.0800.0800.0820.0860.0810.0810.0810.1520.0820.0690.1000.0710.0820.0660.0810.1920.0750.5800.0470.0610.0460.0270.0450.0480.0220.0700.0200.1011.0000.3120.3820.1390.1840.1320.1610.1550.9870.2650.5160.2100.142
country_top_AU0.0231.0001.0000.0210.0320.0360.0320.0540.0460.0510.0240.0950.1380.0030.0000.0070.0010.0050.0000.0000.0010.0060.0000.0040.0060.0000.0020.0080.0000.0000.0000.0030.0080.0000.0060.0010.0210.0350.0030.0340.0000.0010.0060.0000.0040.0050.0020.0000.0000.0020.0000.0020.0000.0010.0020.0010.0020.0020.0020.0030.0010.0040.0020.0020.0000.0070.0080.0060.0030.0010.0010.0080.0030.0010.0030.0000.0010.0000.0010.0050.0000.0030.0040.0000.0040.0000.0060.0020.0000.0030.0020.0000.0010.0030.0000.0000.0020.0080.0070.0020.0040.0030.0100.0080.0010.0050.0060.0010.0090.0070.0010.0030.0010.0010.0000.0030.0060.0040.0010.0080.0020.0070.0010.0000.0020.0010.0020.0030.0010.0060.0040.0120.0080.0050.0030.0010.0060.0090.0050.0050.0040.0000.0040.0010.0000.0000.0000.0100.0020.0000.0040.0110.0050.0010.0000.0010.0030.0000.0020.0040.0010.0000.0040.0030.0240.0110.0200.0060.0020.0000.0040.0070.0230.0380.0190.0060.0050.0060.0020.0070.0020.0030.0000.0040.0071.0000.1360.0530.0190.0250.0180.0220.0210.1360.0360.0710.0290.019
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hg_product_960.2280.0860.0010.0150.0030.0260.0340.0100.0050.0940.0140.0610.0450.1910.1890.1940.1900.1970.1900.1900.1930.1900.2100.1940.2020.1910.1960.1910.1900.1890.1900.2120.2060.1980.1930.1940.2000.0220.1840.1120.2660.9860.7430.7870.7680.7710.7300.7740.7130.7160.7070.7110.7140.7090.7690.7080.7720.7120.7230.7150.7090.7740.7130.7100.7070.7130.7690.7420.7330.7220.7070.7310.7090.7100.7320.7100.7090.7080.7080.7210.7120.7540.7550.7300.7130.7680.7490.7280.7080.7080.7070.7070.7490.7230.7340.7520.7280.7580.7520.7150.7530.7460.7500.7520.7110.7460.7520.7250.7450.7520.7100.7260.7170.7260.7430.7390.7170.7270.7070.7290.7110.7250.7070.7070.7080.7070.7640.7610.7110.7710.7130.7360.7390.7380.7290.7160.7240.7300.7260.7360.7460.7540.7340.7510.7370.7460.7070.7480.7220.7080.7160.7360.7270.7150.7140.7070.7250.7070.7100.7541.0000.7160.7300.7240.0620.0060.0590.0340.0110.0720.0170.0550.0360.0890.1060.0500.0440.0560.2320.4010.3520.1630.5730.1150.8340.0550.0400.0320.0130.0130.0070.0130.0070.0440.0180.0210.0090.009
hg_product_970.1610.0810.0000.0150.0000.0260.0320.0080.0010.0940.0140.0560.0230.1900.1890.1910.1900.1930.1900.1890.1910.1890.1980.1910.1970.1900.1910.1900.1900.1890.1890.1970.2020.1930.1920.1910.2000.0200.1840.1110.2650.9860.7170.7140.7110.7140.7080.7140.7080.7130.7070.7410.7220.7090.7130.7070.7170.7070.7080.7130.7070.7170.7090.7070.7070.7080.7110.7090.7180.7070.7070.7090.7070.7070.7100.7080.7080.7070.7070.7110.7080.7130.7110.7170.7090.7100.7100.7110.7070.7070.7070.7070.7140.7180.7190.7110.7090.7110.7100.7090.7120.7110.7110.7110.7140.7120.7120.7210.7120.7110.7080.7090.7080.7120.7110.7130.7120.7100.7070.7100.7310.7110.7070.7070.7150.7070.7100.7120.7070.7150.7100.7090.7110.7100.7090.7080.7110.7080.7090.7130.7110.7120.7100.7130.7110.7130.7070.7100.7110.7080.7090.7090.7110.7120.7080.7070.7080.7070.7080.7140.7161.0000.7100.7100.0620.0060.0530.0340.0110.0720.0160.0550.0350.0860.1060.0470.0410.0520.1620.3960.3390.1630.5700.1150.8340.0530.0290.0300.0130.0100.0000.0100.0010.0220.0130.0120.0000.007
hg_product_980.1730.0810.0040.0150.0040.0260.0320.0050.0010.0940.0140.0560.0260.1900.1890.1920.1900.1940.1900.1890.1920.1890.2000.1920.1980.1910.1950.1900.1900.1890.1890.2050.2040.1960.1920.1940.2000.0190.1840.1110.2650.9860.7340.7240.7180.7280.7230.7330.7140.7170.7070.7070.7090.7110.7300.7070.7390.7190.7250.7140.7090.7320.7090.7110.7080.7120.7170.7130.7220.7200.7070.7110.7070.7090.7140.7110.7110.7100.7080.7210.7120.7210.7180.7220.7160.7240.7140.7130.7080.7070.7070.7070.7220.7200.7190.7180.7110.7180.7170.7170.7210.7250.7200.7220.7120.7240.7250.7240.7220.7200.7080.7110.7090.7110.7190.7240.7090.7210.7070.7130.7080.7120.7070.7070.7070.7070.7290.7210.7100.7280.7090.7120.7170.7160.7160.7120.7130.7110.7170.7190.7190.7200.7260.7270.7280.7260.7070.7150.7210.7080.7120.7120.7130.7140.7090.7070.7300.7070.7170.7350.7300.7101.0000.7220.0620.0060.0600.0340.0100.0720.0170.0550.0360.0890.1060.0470.0410.0520.1820.3960.3390.1630.5710.1150.8340.0550.0310.0300.0130.0100.0020.0080.0020.0250.0150.0150.0000.009
hg_product_990.1710.0810.0030.0150.0000.0260.0310.0040.0010.0940.0150.0560.0260.1900.1890.1920.1900.1940.1900.1890.1910.1890.1990.1910.1970.1900.1920.1900.1900.1890.1890.1990.2030.1960.1920.1910.2000.0200.1840.1110.2650.9860.7220.7190.7170.7230.7180.7250.7090.7180.7080.7080.7080.7100.7250.7070.7300.7110.7140.7170.7080.7270.7100.7070.7070.7100.7160.7130.7170.7110.7070.7110.7070.7080.7140.7100.7080.7070.7080.7180.7090.7160.7150.7180.7090.7210.7140.7110.7080.7070.7070.7070.7170.7170.7230.7160.7110.7160.7160.7090.7160.7190.7140.7160.7190.7170.7180.7180.7170.7160.7070.7110.7090.7120.7150.7190.7110.7250.7070.7110.7070.7110.7070.7070.7080.7070.7200.7180.7080.7220.7100.7110.7150.7160.7120.7090.7120.7100.7130.7160.7180.7180.7240.7180.7160.7170.7070.7140.7150.7080.7110.7120.7120.7110.7100.7070.7130.7070.7080.7230.7240.7100.7221.0000.0620.0060.0550.0340.0100.0720.0170.0550.0360.0870.1060.0470.0410.0530.1780.3970.3390.1630.5710.1150.8340.0530.0300.0300.0130.0110.0030.0080.0040.0250.0130.0120.0050.009
industry_1. Tech - Computer systems design and related services0.1210.1520.0240.0340.0130.0270.0420.0050.0870.1060.0020.0370.0260.0680.0600.0610.0600.0600.0660.0600.0610.0600.0620.0600.0600.0600.0600.0660.0600.0610.0600.0620.0600.0620.0600.0600.2050.2640.0420.2090.0600.0610.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0630.0620.0640.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0630.0630.0620.0620.0620.0620.0630.0620.0620.0630.0630.0630.0640.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0640.0620.0630.0620.0620.0620.0620.0620.0620.0620.0620.0620.0630.0620.0640.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0620.0640.0620.0620.0630.0630.0630.0630.0630.0620.0620.0620.0620.0620.0620.0620.0620.0621.0000.0420.0880.0720.0810.1050.0740.0640.3281.0000.1450.0130.0130.0130.0170.0120.0360.0000.0480.0060.0610.0740.0360.0110.0030.0120.0020.0070.0000.0230.0220.0200.0030.000
industry_2. Tech - Software Publisher0.0360.0820.0110.0300.0230.0090.0430.0170.0220.0330.0140.0230.0390.0750.0580.0570.0570.0570.0610.0580.0570.0570.0570.0570.0590.0580.0570.0660.0570.0580.0570.0570.0570.0570.0590.0570.0420.0480.0100.0370.0570.0060.0070.0060.0080.0070.0070.0060.0060.0060.0060.0060.0060.0060.0060.0060.0080.0060.0080.0070.0060.0070.0060.0060.0060.0210.0070.0250.0060.0060.0070.0190.0060.0060.0060.0060.0060.0060.0060.0110.0080.0190.0240.0060.0130.0120.0190.0240.0150.0130.0070.0060.0200.0120.0100.0240.0210.0210.0240.0090.0170.0120.0090.0090.0070.0070.0100.0070.0080.0150.0060.0130.0110.0060.0170.0110.0060.0090.0060.0220.0060.0190.0060.0060.0070.0090.0060.0070.0060.0070.0140.0200.0110.0340.0100.0090.0150.0090.0060.0060.0070.0070.0080.0080.0060.0080.0060.0210.0090.0060.0070.0180.0280.0190.0190.0060.0060.0060.0060.0060.0060.0060.0060.0060.0421.0000.0280.0230.0250.0330.0230.0200.1031.0000.0460.0330.0280.0340.0040.0020.0010.0080.0030.0000.0000.0410.0490.0380.0050.0000.0070.0020.0160.0390.0030.0050.0110.009
industry_3. Finance0.0630.0690.0200.0090.0080.0160.0040.0020.0170.0570.0000.0160.0270.0810.0800.0810.0800.0800.0800.0800.0810.0800.0810.0810.0830.0800.0800.0850.0800.0800.0800.0860.0810.0820.0810.0810.0430.0190.0240.0000.0800.0530.0550.0540.0650.0600.0550.0610.0530.0550.0530.0530.0540.0530.0620.0530.0580.0540.0540.0530.0530.0600.0540.0530.0530.0530.0690.0590.0580.0600.0530.0560.0530.0530.0540.0530.0530.0530.0530.0540.0540.0570.0590.0540.0530.0630.0690.0530.0530.0530.0530.0530.0580.0540.0550.0570.0540.0630.0660.0530.0590.0570.0600.0580.0530.0580.0580.0540.0580.0600.0530.0540.0530.0530.0560.0550.0530.0550.0530.0530.0530.0530.0530.0530.0530.0530.0730.0750.0550.0670.0530.0680.0530.0540.0530.0530.0530.0630.0540.0550.0570.0600.0570.0630.0570.0600.0530.0610.0550.0530.0530.0560.0530.0540.0530.0530.0540.0540.0530.0570.0590.0530.0600.0550.0880.0281.0000.0480.0530.0690.0490.0420.2171.0000.0960.0170.0130.0180.0230.0090.0370.0050.0390.0080.0570.0550.0390.0020.0040.0050.0000.0120.0090.0270.0290.0100.0070.005
industry_4. Consulting0.0470.1000.0060.0180.0080.0170.0200.0300.0170.0580.0120.0150.0830.0120.0120.0120.0120.0130.0130.0120.0120.0120.0130.0120.0120.0120.0120.0140.0120.0120.0120.0130.0120.0120.0120.0120.0710.0560.0280.0310.0120.0330.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0340.0720.0230.0481.0000.0440.0570.0400.0350.1781.0000.0790.0030.0020.0020.0070.0090.0190.0030.0260.0030.0350.0480.0870.0130.0150.0270.0180.0210.0150.0830.0130.0520.0220.006
industry_5. Retail0.0360.0710.0020.0030.0070.0100.0110.0400.0200.0540.0050.0020.0140.0300.0280.0280.0280.0280.0300.0280.0280.0280.0280.0280.0280.0280.0280.0370.0280.0280.0280.0280.0300.0280.0280.0280.0170.0100.0220.0250.0280.0110.0110.0110.0120.0110.0110.0120.0110.0100.0100.0110.0100.0100.0100.0100.0100.0110.0100.0100.0100.0110.0120.0110.0100.0120.0120.0140.0100.0110.0100.0110.0100.0100.0100.0110.0110.0100.0100.0110.0110.0110.0110.0100.0100.0110.0100.0130.0100.0100.0100.0100.0110.0110.0110.0100.0100.0120.0100.0110.0110.0100.0100.0100.0100.0100.0100.0100.0100.0100.0100.0200.0120.0180.0170.0120.0100.0100.0110.0130.0110.0130.0100.0100.0110.0100.0100.0100.0100.0110.0110.0110.0120.0120.0100.0100.0180.0130.0110.0120.0110.0110.0100.0100.0110.0100.0100.0110.0100.0100.0100.0110.0110.0100.0100.0100.0110.0100.0110.0100.0110.0110.0100.0100.0810.0250.0530.0441.0000.0640.0450.0390.1991.0000.0880.0120.0100.0110.0080.0000.0100.0020.0130.0000.0150.0350.0210.0080.0030.0030.0020.0020.0050.0160.0140.0080.0010.001
industry_6. Manufacturing0.0840.0820.0000.0140.0030.0290.0130.0100.0450.0590.0160.0020.0080.0280.0260.0260.0260.0260.0320.0260.0270.0260.0280.0260.0260.0260.0260.0280.0260.0260.0260.0260.0260.0270.0260.0260.1590.0270.1330.0690.0260.0730.0730.0720.0720.0730.0720.0720.0720.0720.0720.0720.0720.0720.0730.0720.0730.0720.0720.0720.0720.0730.0760.0720.0720.0730.0730.0740.0720.0720.0720.0730.0720.0730.0850.0720.0720.0720.0720.0720.0720.0720.0720.0720.0720.0720.0730.0730.0720.0720.0720.0720.0730.0720.0720.0720.0770.0730.0730.0720.0720.0720.0720.0720.0720.0720.0720.0720.0720.0720.0720.0730.0740.0720.0740.0730.0730.0720.0720.0720.0720.0720.0720.0720.0720.0720.0730.0720.0720.0720.0720.0720.0730.0720.0720.0720.0730.0730.0730.0740.0730.0730.0720.0730.0720.0720.0720.0730.0720.0720.0720.0740.0720.0720.0720.0720.0720.0720.0720.0720.0720.0720.0720.0720.1050.0330.0690.0570.0641.0000.0590.0500.2591.0000.1150.0070.0030.0090.0290.0220.0560.0030.0650.0000.0900.0720.0270.0090.0040.0090.0020.0050.0100.0100.0100.0170.0070.000
industry_7. Wholesale Trade0.0170.0660.0040.0150.0040.0030.0080.0070.0270.0540.0050.0270.0000.0140.0120.0120.0120.0130.0170.0130.0120.0120.0120.0120.0130.0130.0120.0130.0120.0130.0120.0140.0130.0120.0120.0120.0780.0250.0980.0920.0130.0170.0160.0160.0190.0180.0160.0180.0160.0160.0160.0160.0160.0160.0170.0160.0170.0160.0160.0160.0160.0180.0170.0160.0160.0180.0200.0200.0160.0160.0160.0160.0160.0160.0160.0160.0160.0160.0160.0160.0160.0170.0170.0170.0160.0170.0180.0170.0160.0160.0160.0160.0180.0160.0170.0180.0220.0190.0190.0160.0180.0170.0160.0170.0160.0160.0170.0160.0160.0170.0160.0160.0170.0160.0160.0160.0160.0160.0160.0180.0160.0170.0160.0160.0160.0160.0180.0170.0160.0170.0160.0170.0180.0170.0160.0160.0160.0180.0180.0180.0180.0190.0170.0190.0160.0180.0160.0190.0170.0160.0160.0200.0160.0160.0160.0160.0170.0160.0160.0160.0170.0160.0170.0170.0740.0230.0490.0400.0450.0591.0000.0360.1831.0000.0810.0060.0020.0050.0050.0050.0120.0020.0160.0000.0210.0340.0100.0050.0030.0000.0000.0050.0020.0020.0020.0070.0020.000
industry_8. Healthcare0.0550.0810.0070.0020.0030.0030.0000.0150.0210.0430.0030.0370.0710.0330.0330.0340.0330.0330.0330.0330.0330.0330.0330.0330.0330.0330.0330.0370.0330.0330.0330.0330.0330.0330.0330.0330.0570.0260.0310.0010.0330.0560.0550.0550.0570.0560.0550.0560.0550.0550.0550.0550.0550.0550.0550.0550.0560.0550.0550.0550.0550.0560.0550.0550.0550.0550.0570.0580.0550.0550.0550.0560.0550.0550.0600.0550.0550.0550.0550.0550.0550.0550.0560.0550.0550.0550.0570.0560.0550.0550.0550.0550.0560.0550.0560.0560.0600.0560.0580.0550.0560.0550.0550.0560.0550.0550.0550.0550.0550.0560.0550.0560.0550.0560.0570.0560.0550.0550.0550.0560.0550.0550.0550.0550.0550.0550.0550.0560.0550.0550.0550.0590.0560.0550.0550.0550.0560.0560.0550.0550.0550.0550.0550.0550.0550.0550.0550.0590.0550.0550.0550.0570.0550.0560.0550.0550.0550.0550.0550.0550.0550.0550.0550.0550.0640.0200.0420.0350.0390.0500.0361.0000.1571.0000.0700.0040.0030.0040.0020.0260.0090.0030.0410.0040.0460.0490.0750.0110.0110.0150.0030.0120.0150.0710.0150.0520.0130.012
industry_9. Other0.0560.1920.0230.0220.0320.0000.0150.0360.0200.1830.0240.0220.0460.0190.0170.0170.0170.0180.0170.0180.0180.0170.0170.0170.0190.0180.0170.0190.0170.0170.0170.0170.0170.0170.0180.0170.0250.0180.0090.0100.0170.0370.0350.0360.0360.0360.0360.0360.0350.0360.0350.0350.0350.0360.0370.0360.0360.0350.0350.0350.0360.0360.0380.0360.0350.0360.0360.0360.0360.0360.0350.0410.0350.0350.0380.0350.0360.0360.0350.0360.0360.0380.0400.0360.0360.0390.0400.0360.0360.0360.0360.0350.0370.0360.0360.0380.0370.0380.0400.0350.0370.0380.0370.0380.0350.0370.0370.0360.0370.0390.0360.0360.0360.0350.0370.0360.0350.0360.0350.0360.0350.0360.0350.0360.0350.0350.0390.0410.0360.0380.0350.0420.0360.0390.0360.0360.0360.0360.0350.0350.0360.0370.0370.0370.0360.0360.0350.0370.0360.0350.0360.0350.0370.0360.0360.0350.0350.0350.0350.0360.0360.0350.0360.0360.3280.1030.2170.1780.1990.2590.1830.1571.0001.0000.3580.0150.0140.0140.0010.0190.0010.0080.0120.0100.0210.0420.0470.0030.0130.0150.0060.0050.0000.0430.0190.0240.0130.008
industry_grouped0.0820.0750.0380.0600.0380.0490.0660.0600.1090.0730.0320.0670.1150.0930.0830.0830.0820.0820.0870.0820.0820.0820.0830.0820.0840.0820.0820.0920.0820.0820.0820.0860.0830.0830.0830.0820.2720.2550.1640.2090.1160.1220.0870.0870.0920.0900.0870.0900.0870.0870.0860.0860.0870.0870.0900.0860.0890.0870.0870.0870.0860.0900.0890.0860.0860.0890.0930.0930.0880.0890.0860.0900.0870.0870.0940.0870.0870.0860.0860.0870.0870.0890.0910.0870.0870.0910.0950.0890.0870.0870.0860.0870.0900.0870.0880.0900.0930.0920.0950.0870.0900.0880.0890.0890.0860.0880.0880.0870.0880.0900.0870.0890.0880.0880.0900.0880.0870.0880.0860.0890.0870.0880.0860.0860.0870.0870.0940.0950.0870.0920.0870.0950.0880.0910.0870.0870.0880.0910.0870.0880.0880.0890.0880.0910.0880.0890.0860.0930.0870.0860.0870.0900.0890.0880.0880.0860.0870.0870.0870.0880.0890.0860.0890.0871.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0300.0370.0310.0420.0360.0770.0100.0980.0120.1300.0530.0470.0420.0220.0350.0190.0270.0280.1140.0450.0730.0280.016
industry_nan0.0530.5800.0190.0180.0300.0160.0440.0580.0510.5770.0150.0590.1700.1580.1580.1580.1580.1580.1580.1580.1580.1580.1580.1580.1580.1580.1580.1580.1580.1580.1580.1580.1580.1580.1580.1580.0000.2420.1590.3300.1580.1120.1060.1060.1060.1060.1060.1060.1060.1060.1060.1070.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1060.1450.0460.0960.0790.0880.1150.0810.0700.3581.0001.0000.0250.0150.0270.0200.0510.0440.0210.0700.0130.1050.0710.1740.0520.0250.0310.0240.0340.0310.1730.0410.1020.0360.023
is_arr_over_12k0.0240.0470.0060.0060.0050.0060.0060.0030.0280.0340.0040.0240.0560.1250.1540.1120.1070.1060.1170.1270.1110.1330.1130.1090.1970.1230.1070.1160.1210.1340.1100.1070.1130.1060.1680.1060.0340.0150.0210.0130.1490.0630.0470.0480.0500.0470.0470.0480.0470.0470.0460.0460.0470.0470.0480.0460.0480.0470.0470.0470.0460.0480.0470.0470.0460.0550.0500.0660.0490.0490.0470.0680.0470.0470.0480.0470.0470.0470.0470.0480.0480.0860.0920.0490.0560.0550.0680.0700.0540.0530.0480.0460.0640.0520.0510.0760.0610.0590.0680.0480.0600.0510.0520.0520.0480.0510.0530.0470.0500.0610.0470.0590.0550.0530.0710.0530.0480.0480.0490.0640.0470.0620.0470.0470.0470.0460.0480.0500.0470.0480.0610.0670.0480.0730.0540.0490.0590.0520.0460.0470.0480.0490.0480.0480.0480.0490.0470.0610.0490.0470.0470.0580.0770.0560.0590.0470.0470.0460.0460.0470.0500.0470.0470.0470.0130.0330.0170.0030.0120.0070.0060.0040.0150.0300.0251.0001.0000.8580.0270.0220.0390.0110.0410.0000.0660.0390.0480.0440.0060.0000.0020.0090.0180.0560.0340.0080.0080.006
is_current_customer0.0330.0610.0050.0040.0040.0020.0030.0030.0280.0340.0010.0190.0500.1650.1950.1540.1490.1480.1630.1640.1520.1780.1530.1510.2440.1660.1500.1590.1580.1750.1510.1490.1570.1490.1980.1480.0320.0130.0190.0000.1480.0380.0400.0410.0430.0410.0400.0420.0400.0400.0400.0400.0400.0400.0410.0400.0420.0400.0400.0410.0400.0410.0410.0400.0400.0490.0430.0620.0420.0430.0410.0670.0400.0410.0420.0400.0410.0400.0400.0420.0410.0810.0920.0430.0520.0500.0660.0690.0480.0470.0420.0400.0600.0450.0450.0760.0600.0550.0650.0430.0560.0460.0470.0470.0420.0460.0470.0410.0440.0580.0400.0560.0520.0480.0720.0490.0420.0410.0430.0640.0410.0600.0410.0400.0420.0400.0420.0440.0400.0420.0560.0650.0420.0710.0490.0430.0540.0470.0400.0400.0410.0430.0420.0420.0420.0430.0400.0580.0440.0400.0420.0540.0760.0510.0550.0400.0400.0400.0400.0410.0440.0410.0410.0410.0130.0280.0130.0020.0100.0030.0020.0030.0140.0370.0151.0001.0001.0000.0180.0120.0250.0070.0230.0000.0390.0550.0620.0410.0060.0000.0030.0070.0160.0500.0320.0030.0070.006
is_self_service0.0280.0460.0060.0070.0040.0080.0070.0030.0280.0340.0030.0230.0550.1260.1520.1120.1060.1060.1160.1280.1120.1320.1150.1100.2030.1220.1070.1160.1220.1340.1110.1070.1140.1070.1710.1050.0370.0150.0240.0170.1490.0710.0520.0530.0550.0530.0520.0540.0520.0520.0520.0520.0520.0520.0540.0520.0540.0520.0520.0520.0520.0530.0530.0520.0520.0600.0560.0710.0540.0540.0520.0730.0520.0520.0540.0520.0520.0520.0520.0530.0530.0920.0990.0540.0600.0610.0730.0740.0590.0580.0530.0520.0700.0560.0560.0820.0670.0640.0740.0540.0660.0570.0580.0580.0530.0560.0600.0530.0550.0670.0520.0630.0600.0590.0760.0590.0530.0530.0550.0690.0520.0670.0530.0520.0530.0520.0540.0550.0520.0530.0660.0730.0530.0780.0600.0540.0630.0570.0520.0520.0530.0550.0530.0540.0540.0540.0520.0670.0550.0520.0530.0630.0810.0600.0640.0520.0520.0520.0520.0530.0560.0520.0520.0530.0130.0340.0180.0020.0110.0090.0050.0040.0140.0310.0270.8581.0001.0000.0310.0220.0460.0130.0460.0040.0750.0390.0470.0430.0070.0000.0020.0090.0190.0550.0330.0090.0070.006
revenue_Over 10B1.0000.0270.0020.0000.0000.0030.0080.0070.0100.0200.0010.0110.0080.0650.0640.0750.0670.0900.0640.0650.0850.0640.1250.0740.0910.0750.0950.0640.0650.0640.0640.1340.1070.1120.0710.0840.0510.0120.0420.0180.0610.1400.2040.1990.2280.2280.1860.2330.1640.1630.1520.1560.1650.1530.2340.1520.2220.1600.1730.1580.1550.2320.1680.1560.1530.1580.2290.2120.1910.1790.1530.1910.1620.1700.2580.1750.1600.1550.1530.1830.1660.2170.2240.1920.1640.2450.2280.1920.1560.1560.1520.1520.2140.1790.1930.2240.1960.2430.2370.1690.2270.2330.2370.2510.1620.2510.2370.1800.2330.2510.1630.1930.1750.1970.2200.2160.1690.1910.1520.1900.1560.1800.1520.1520.1560.1530.2270.2410.1610.2410.1600.1990.2130.2000.1860.1630.1920.1870.1790.1870.2050.2150.1980.2170.1980.2160.1520.2210.1830.1540.1730.2020.1850.1660.1680.1520.1840.1530.1560.2250.2320.1620.1820.1780.0170.0040.0230.0070.0080.0290.0050.0020.0010.0420.0200.0270.0180.0311.0000.0090.0090.0040.0140.0020.2370.0250.0130.0000.0020.0020.0020.0030.0000.0080.0000.0060.0070.006
revenue_Under 100M1.0000.0450.0070.0020.0020.0050.0160.0110.0060.0400.0020.0100.0020.0960.0940.0940.0940.0950.0950.0950.0940.0940.0950.0940.0950.0940.0940.0950.0950.0940.0940.0940.0960.0940.0950.0940.0780.0020.0780.0520.0950.4020.3970.3970.4100.4010.3960.3980.3960.3960.3960.3960.3960.3960.3980.3960.3970.3960.3960.3960.3960.3990.3970.3960.3960.3960.4060.4030.3970.3970.3960.4010.3960.3960.4030.3970.3960.3960.3960.3960.3960.3980.3990.3970.3960.4000.4030.3990.3960.3960.3960.3960.3970.3970.3970.3980.4030.4030.4010.3960.3980.3980.4010.4000.3960.3990.3990.3960.4000.3990.3960.4050.4240.3970.3990.3970.3970.3970.3960.3980.3960.3970.3960.3960.3960.3960.3990.4030.3960.3990.3960.4040.3980.3970.3970.3960.3980.4120.4000.4010.4030.4020.3970.3990.3970.3980.3960.4020.3970.3960.3970.4070.3960.3960.3960.3960.3960.3960.3960.3980.4010.3960.3960.3970.0120.0020.0090.0090.0000.0220.0050.0260.0190.0360.0510.0220.0120.0220.0091.0000.0180.0080.0270.0050.4600.0290.0120.0080.0050.0020.0020.0010.0000.0010.0000.0070.0000.004
revenue_Under 10B1.0000.0480.0020.0030.0000.0120.0050.0080.0160.0370.0000.0270.0050.1330.1330.1390.1340.1400.1330.1330.1370.1330.1550.1390.1520.1330.1340.1330.1340.1330.1330.1490.1680.1410.1370.1350.0990.0110.0970.0580.1310.3360.3400.3410.3690.3460.3390.3480.3390.3400.3380.3380.3380.3390.3450.3380.3450.3380.3400.3400.3380.3450.3400.3390.3380.3380.3640.3550.3400.3400.3380.3610.3390.3390.3660.3390.3390.3380.3380.3390.3390.3490.3570.3400.3390.3520.3750.3410.3380.3380.3380.3380.3430.3390.3400.3490.3500.3630.3630.3390.3490.3450.3600.3560.3390.3490.3490.3400.3530.3570.3390.3470.3480.3430.3520.3410.3390.3390.3380.3440.3380.3410.3380.3380.3380.3380.3480.3590.3390.3460.3390.3670.3450.3450.3410.3390.3410.3640.3380.3390.3420.3460.3400.3470.3430.3490.3380.3550.3390.3380.3390.3510.3410.3390.3390.3380.3390.3380.3390.3430.3520.3390.3390.3390.0360.0010.0370.0190.0100.0560.0120.0090.0010.0770.0440.0390.0250.0460.0090.0181.0000.0070.0250.0050.4310.0360.0180.0080.0030.0020.0000.0080.0010.0050.0030.0140.0020.005
revenue_Under 10M1.0000.0220.0030.0030.0010.0010.0110.0080.0010.0170.0020.0050.0050.0430.0410.0410.0410.0420.0410.0410.0410.0420.0420.0430.0430.0410.0410.0420.0420.0420.0410.0420.0430.0420.0430.0410.0140.0070.0190.0200.0410.1650.1630.1630.1650.1640.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1650.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1640.1730.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1640.1630.1630.1630.1630.1630.1630.1630.1630.1630.1670.1640.1650.1650.1640.1630.1630.1630.1630.1630.1630.1630.1630.1630.1640.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.1630.0000.0080.0050.0030.0020.0030.0020.0030.0080.0100.0210.0110.0070.0130.0040.0080.0071.0000.0110.0010.1950.0100.0060.0000.0030.0020.0000.0000.0030.0040.0010.0030.0000.000
revenue_Under 1B1.0000.0700.0000.0000.0000.0250.0170.0000.0110.0570.0100.0320.0080.1720.1720.1730.1720.1730.1740.1720.1720.1720.1740.1720.1750.1720.1720.1750.1720.1720.1720.1730.1760.1720.1740.1720.1440.0160.1380.0820.1720.5760.5720.5710.5760.5750.5710.5720.5700.5700.5700.5700.5700.5700.5720.5700.5720.5700.5700.5700.5700.5730.5700.5700.5700.5700.5760.5750.5700.5700.5700.5720.5700.5700.5730.5700.5700.5700.5700.5700.5700.5720.5720.5710.5700.5750.5750.5750.5700.5710.5700.5700.5720.5710.5710.5740.5760.5770.5760.5700.5730.5720.5720.5730.5700.5720.5720.5700.5710.5730.5700.5770.5840.5710.5740.5720.5720.5710.5700.5730.5700.5710.5700.5700.5700.5700.5720.5750.5700.5730.5700.5730.5720.5720.5710.5700.5710.5720.5760.5770.5780.5750.5720.5730.5720.5720.5700.5760.5710.5700.5710.5790.5710.5700.5700.5700.5700.5700.5700.5720.5730.5700.5710.5710.0480.0030.0390.0260.0130.0650.0160.0410.0120.0980.0700.0410.0230.0460.0140.0270.0250.0111.0000.0070.6580.0560.0270.0190.0080.0070.0010.0060.0040.0080.0090.0120.0010.003
revenue_Under 1M1.0000.0200.0040.0040.0000.0060.0090.0040.0020.0110.0070.0070.0080.0290.0290.0290.0290.0290.0290.0290.0290.0290.0290.0290.0310.0290.0300.0290.0290.0290.0290.0310.0300.0300.0290.0290.0020.0060.0070.0100.0280.1160.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1160.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1160.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1160.1190.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1160.1160.1160.1160.1150.1160.1150.1150.1150.1150.1150.1150.1150.1150.1160.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.1150.0060.0000.0080.0030.0000.0000.0000.0040.0100.0120.0130.0000.0000.0040.0020.0050.0050.0010.0071.0000.1310.0000.0070.0030.0010.0010.0010.0000.0000.0080.0000.0040.0000.002
revenue_nan1.0000.1010.0070.0000.0000.0270.0280.0000.0140.0860.0090.0450.0050.2570.2560.2590.2560.2610.2570.2560.2580.2560.2700.2580.2690.2560.2580.2580.2570.2560.2560.2670.2770.2620.2600.2570.2040.0180.1940.1200.2540.8380.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8350.8340.8340.8340.8340.8340.8340.8350.8350.8340.8340.8340.8350.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8350.8350.8340.8350.8350.8340.8350.8350.8340.8350.8430.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8370.8370.8360.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8350.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.8340.0610.0000.0570.0350.0150.0900.0210.0460.0210.1300.1050.0660.0390.0750.2370.4600.4310.1950.6580.1311.0000.0720.0340.0220.0120.0080.0000.0090.0050.0040.0080.0180.0030.005
state0.0471.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0710.0700.0710.0690.0690.0770.0700.0720.0700.0720.0690.0730.0710.0680.0770.0690.0700.0690.0690.0690.0690.0690.0690.2420.1280.1630.1630.0970.0760.0560.0540.0550.0550.0530.0550.0540.0540.0550.0530.0530.0520.0550.0550.0560.0540.0540.0540.0560.0550.0530.0550.0530.0540.0540.0590.0540.0530.0520.0580.0530.0530.0550.0530.0530.0520.0530.0550.0530.0560.0560.0530.0530.0540.0560.0610.0540.0540.0520.0530.0560.0550.0550.0590.0570.0580.0590.0540.0560.0550.0540.0540.0530.0540.0530.0540.0530.0560.0530.0560.0540.0540.0600.0550.0530.0540.0530.0560.0530.0560.0520.0530.0520.0520.0560.0540.0530.0540.0560.0590.0560.0590.0550.0530.0550.0540.0540.0540.0550.0540.0530.0550.0540.0540.0530.0580.0540.0520.0540.0560.0580.0550.0540.0520.0530.0520.0520.0540.0550.0530.0550.0530.0740.0410.0550.0480.0350.0720.0340.0490.0420.0530.0710.0390.0550.0390.0250.0290.0360.0100.0560.0000.0721.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
state_top0.0320.3120.1360.0940.1400.1570.1390.2380.2020.2240.1050.4160.9870.1570.1570.1580.1560.1570.1580.1570.1570.1580.1580.1560.1570.1570.1560.1580.1560.1570.1560.1560.1580.1570.1560.1560.2370.2520.0730.2360.2210.0420.0310.0320.0380.0340.0290.0340.0290.0290.0280.0280.0280.0290.0330.0280.0340.0300.0300.0300.0280.0320.0300.0290.0280.0430.0380.0380.0320.0300.0280.0670.0280.0290.0280.0280.0290.0280.0280.0330.0320.0490.0550.0300.0320.0440.0400.0500.0320.0300.0290.0280.0420.0340.0340.0520.0460.0410.0380.0310.0410.0380.0370.0360.0290.0340.0400.0310.0350.0380.0290.0370.0430.0290.0360.0350.0310.0320.0280.0570.0290.0470.0300.0280.0290.0290.0340.0400.0280.0360.0390.0430.0290.0580.0360.0320.0370.0500.0350.0390.0420.0430.0360.0390.0350.0370.0280.0380.0320.0280.0300.0390.0580.0380.0390.0280.0310.0280.0280.0320.0400.0290.0310.0300.0360.0490.0390.0870.0210.0270.0100.0750.0470.0470.1740.0480.0620.0470.0130.0120.0180.0060.0270.0070.0341.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
state_top_CA0.0310.3820.0530.0360.0540.0610.0540.0920.0780.0870.0410.1610.3820.1160.1150.1150.1140.1150.1150.1150.1140.1160.1140.1140.1150.1170.1140.1160.1140.1160.1140.1140.1150.1150.1140.1140.0200.0580.0230.0690.1140.0310.0300.0300.0310.0310.0300.0300.0300.0300.0300.0300.0300.0300.0310.0300.0320.0300.0300.0300.0300.0300.0300.0300.0300.0370.0310.0360.0300.0300.0300.0420.0300.0300.0300.0300.0300.0300.0300.0340.0310.0410.0410.0300.0310.0350.0350.0450.0330.0310.0300.0300.0380.0340.0310.0420.0390.0360.0370.0310.0360.0330.0310.0310.0300.0300.0330.0300.0310.0340.0300.0330.0340.0310.0360.0340.0310.0330.0300.0390.0300.0370.0300.0300.0310.0300.0300.0310.0300.0310.0370.0370.0310.0450.0360.0320.0330.0320.0300.0310.0310.0320.0310.0310.0300.0310.0300.0360.0330.0300.0320.0350.0440.0350.0370.0300.0300.0300.0300.0300.0320.0300.0300.0300.0110.0380.0020.0130.0080.0090.0050.0110.0030.0420.0520.0440.0410.0430.0000.0080.0080.0000.0190.0030.0221.0001.0001.0000.0320.0430.0310.0370.0360.3870.0610.1200.0490.033
state_top_CO0.0000.1390.0190.0130.0200.0220.0200.0330.0280.0310.0150.0590.1390.0100.0100.0110.0100.0110.0110.0100.0100.0120.0160.0100.0110.0100.0100.0100.0100.0100.0100.0100.0120.0110.0100.0100.0190.0300.0070.0320.0100.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0150.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0140.0130.0130.0130.0140.0140.0130.0130.0130.0130.0140.0130.0130.0140.0130.0130.0130.0130.0130.0130.0140.0130.0130.0130.0140.0130.0130.0130.0130.0140.0140.0130.0130.0130.0130.0130.0130.0150.0130.0150.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0150.0130.0130.0130.0140.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0130.0140.0140.0140.0130.0130.0130.0130.0130.0130.0130.0130.0130.0030.0050.0040.0150.0030.0040.0030.0110.0130.0220.0250.0060.0060.0070.0020.0050.0030.0030.0080.0010.0121.0001.0000.0321.0000.0150.0110.0130.0130.1410.0220.0440.0180.012
state_top_FL0.0000.1840.0250.0170.0260.0290.0260.0440.0380.0420.0190.0780.1840.0150.0150.0160.0170.0160.0170.0150.0150.0150.0150.0150.0160.0150.0150.0160.0150.0150.0150.0160.0180.0150.0160.0160.0250.0350.0040.0340.0150.0100.0110.0120.0130.0130.0110.0110.0100.0100.0100.0100.0110.0100.0110.0100.0110.0100.0100.0100.0100.0120.0110.0100.0110.0120.0120.0110.0100.0100.0100.0180.0110.0100.0100.0100.0100.0110.0100.0100.0120.0120.0130.0100.0100.0130.0120.0120.0110.0100.0100.0100.0130.0100.0120.0120.0120.0110.0110.0100.0110.0120.0130.0110.0120.0110.0120.0120.0120.0110.0120.0120.0120.0110.0110.0110.0120.0100.0100.0160.0100.0130.0100.0100.0110.0100.0120.0140.0100.0120.0110.0120.0100.0130.0110.0110.0120.0150.0160.0160.0150.0150.0130.0130.0120.0130.0100.0110.0110.0100.0110.0110.0150.0100.0120.0100.0110.0100.0100.0120.0130.0100.0100.0110.0120.0000.0050.0270.0030.0090.0000.0150.0150.0350.0310.0000.0000.0000.0020.0020.0020.0020.0070.0010.0081.0001.0000.0430.0151.0000.0150.0180.0170.1870.0300.0580.0230.016
state_top_GA0.0120.1320.0180.0120.0190.0210.0190.0320.0270.0300.0140.0560.1320.0230.0220.0250.0220.0220.0230.0220.0230.0220.0250.0220.0220.0220.0220.0220.0220.0220.0220.0220.0230.0220.0220.0220.0300.0300.0010.0260.0220.0000.0040.0020.0040.0050.0000.0050.0010.0050.0000.0000.0010.0020.0060.0030.0050.0100.0020.0000.0000.0060.0000.0030.0000.0040.0060.0030.0070.0010.0000.0130.0040.0000.0000.0000.0010.0000.0000.0030.0020.0060.0090.0020.0040.0070.0030.0080.0010.0000.0030.0000.0060.0050.0060.0070.0070.0030.0020.0000.0070.0080.0070.0070.0000.0070.0050.0070.0080.0070.0020.0040.0090.0000.0040.0040.0040.0000.0000.0110.0000.0100.0020.0000.0000.0080.0050.0070.0000.0050.0020.0050.0000.0130.0050.0030.0020.0070.0050.0080.0080.0100.0060.0060.0070.0060.0000.0030.0020.0000.0000.0040.0120.0100.0060.0000.0050.0000.0030.0040.0070.0000.0020.0030.0020.0070.0000.0180.0020.0020.0000.0030.0060.0190.0240.0020.0030.0020.0020.0020.0000.0000.0010.0010.0001.0001.0000.0310.0110.0151.0000.0130.0120.1340.0210.0410.0170.011
state_top_IL0.0150.1610.0220.0150.0230.0260.0230.0390.0330.0360.0170.0680.1610.0350.0350.0370.0350.0350.0350.0350.0360.0350.0360.0350.0350.0350.0350.0350.0350.0350.0350.0350.0350.0350.0350.0350.0670.0450.0200.0260.0350.0070.0080.0090.0120.0090.0080.0100.0070.0070.0070.0070.0070.0070.0090.0070.0080.0070.0070.0070.0070.0090.0080.0070.0070.0120.0110.0120.0110.0080.0070.0240.0080.0070.0080.0070.0070.0070.0070.0070.0070.0150.0170.0080.0090.0150.0130.0150.0120.0110.0080.0070.0120.0090.0110.0150.0120.0140.0110.0080.0120.0150.0140.0140.0070.0110.0140.0100.0120.0100.0100.0130.0140.0070.0120.0110.0080.0080.0080.0150.0070.0100.0090.0080.0070.0070.0150.0130.0070.0120.0090.0160.0070.0190.0110.0080.0140.0190.0110.0120.0160.0150.0100.0130.0120.0120.0070.0110.0080.0070.0070.0140.0200.0100.0110.0070.0070.0070.0070.0090.0130.0100.0080.0080.0070.0020.0120.0210.0020.0050.0050.0120.0050.0270.0340.0090.0070.0090.0030.0010.0080.0000.0060.0000.0091.0001.0000.0370.0130.0180.0131.0000.0150.1630.0260.0500.0200.014
state_top_MA0.0000.1550.0210.0150.0220.0240.0220.0370.0320.0350.0160.0650.1550.0520.0500.0500.0500.0500.0500.0500.0500.0520.0500.0500.0540.0500.0500.0500.0500.0500.0500.0500.0500.0500.0500.0500.0280.0330.0010.0310.0500.0010.0050.0060.0080.0060.0040.0070.0010.0050.0010.0020.0010.0010.0040.0010.0050.0020.0030.0060.0010.0030.0050.0010.0010.0050.0090.0110.0040.0030.0010.0200.0020.0030.0010.0010.0030.0010.0010.0030.0050.0140.0180.0040.0070.0100.0100.0120.0010.0020.0010.0010.0100.0060.0090.0160.0130.0120.0110.0040.0110.0020.0060.0070.0020.0050.0080.0060.0060.0110.0030.0080.0100.0040.0060.0060.0030.0060.0010.0210.0050.0220.0110.0010.0020.0020.0020.0080.0040.0060.0020.0120.0060.0210.0060.0050.0070.0090.0030.0040.0090.0070.0070.0080.0040.0050.0010.0110.0070.0010.0010.0090.0160.0060.0050.0030.0030.0010.0020.0050.0070.0010.0020.0040.0000.0160.0090.0150.0050.0100.0020.0150.0000.0280.0310.0180.0160.0190.0000.0000.0010.0030.0040.0000.0051.0001.0000.0360.0130.0170.0120.0151.0000.1570.0250.0480.0200.013
state_top_Missing0.0510.9870.1360.0940.1400.1570.1390.2380.2020.2240.1050.4160.9870.2150.2140.2170.2140.2150.2140.2140.2140.2160.2150.2140.2140.2140.2140.2150.2140.2140.2140.2140.2160.2150.2140.2140.2100.2470.0050.2270.2140.0220.0260.0290.0400.0330.0240.0330.0210.0220.0200.0200.0200.0200.0310.0200.0330.0210.0230.0230.0200.0290.0240.0200.0200.0480.0400.0380.0280.0250.0200.0870.0200.0200.0200.0200.0200.0200.0200.0300.0290.0570.0680.0240.0280.0520.0430.0590.0240.0240.0210.0200.0470.0300.0320.0620.0530.0450.0380.0250.0460.0400.0390.0360.0230.0310.0440.0270.0350.0390.0210.0390.0500.0220.0330.0320.0270.0270.0200.0730.0200.0540.0200.0200.0210.0210.0300.0440.0200.0380.0370.0490.0210.0720.0350.0270.0380.0600.0350.0410.0480.0500.0360.0420.0350.0390.0200.0400.0270.0200.0220.0420.0730.0380.0420.0200.0260.0200.0200.0280.0440.0220.0250.0250.0230.0390.0270.0830.0160.0100.0020.0710.0430.1140.1730.0560.0500.0550.0080.0010.0050.0040.0080.0080.0041.0001.0000.3870.1410.1870.1340.1630.1571.0000.2680.5230.2130.143
state_top_NY0.0170.2650.0360.0250.0380.0420.0370.0640.0540.0600.0280.1110.2650.0770.0780.0770.0770.0770.0780.0770.0770.0780.0770.0770.0770.0770.0770.0770.0770.0770.0770.0770.0770.0770.0770.0770.0180.0940.0530.1220.0760.0130.0150.0150.0140.0160.0130.0150.0140.0130.0120.0120.0120.0140.0140.0130.0160.0130.0130.0130.0120.0140.0120.0140.0120.0150.0150.0180.0150.0140.0120.0280.0120.0120.0130.0130.0140.0130.0120.0180.0140.0240.0260.0130.0180.0210.0180.0260.0130.0150.0140.0120.0200.0180.0160.0270.0200.0190.0170.0180.0220.0180.0150.0160.0130.0140.0150.0130.0140.0190.0120.0190.0190.0140.0210.0190.0140.0140.0120.0230.0130.0210.0120.0130.0120.0120.0170.0160.0130.0160.0220.0200.0130.0260.0160.0150.0180.0150.0140.0160.0170.0170.0150.0160.0170.0170.0120.0180.0150.0120.0140.0170.0300.0190.0170.0120.0160.0120.0130.0150.0180.0130.0150.0130.0220.0030.0290.0130.0140.0100.0020.0150.0190.0450.0410.0340.0320.0330.0000.0000.0030.0010.0090.0000.0081.0001.0000.0610.0220.0300.0210.0260.0250.2681.0000.0830.0340.023
state_top_Other0.0380.5160.0710.0490.0730.0820.0730.1240.1050.1170.0550.2170.5160.0890.0900.0910.0890.0900.0930.0900.0900.0900.0900.0900.0890.0900.0890.0950.0890.0900.0900.0890.0910.0900.0890.0890.1970.1510.0420.1070.0900.0100.0140.0150.0250.0180.0120.0190.0110.0110.0110.0110.0100.0100.0170.0100.0160.0130.0140.0140.0110.0160.0130.0110.0100.0230.0240.0130.0130.0130.0100.0430.0100.0110.0110.0100.0100.0100.0100.0130.0140.0220.0290.0120.0110.0250.0170.0180.0100.0100.0100.0110.0180.0110.0140.0220.0220.0180.0120.0110.0180.0170.0200.0150.0110.0150.0210.0120.0180.0140.0100.0160.0260.0100.0100.0120.0110.0110.0100.0350.0110.0210.0100.0100.0100.0100.0150.0280.0100.0210.0110.0190.0100.0260.0120.0110.0160.0390.0170.0210.0240.0260.0190.0210.0200.0200.0110.0140.0110.0100.0100.0180.0280.0120.0170.0100.0130.0100.0100.0140.0210.0120.0150.0120.0200.0050.0100.0520.0080.0170.0070.0520.0240.0730.1020.0080.0030.0090.0060.0070.0140.0030.0120.0040.0181.0001.0000.1200.0440.0580.0410.0500.0480.5230.0831.0000.0660.044
state_top_TX0.0210.2100.0290.0200.0300.0330.0300.0510.0430.0480.0220.0880.2100.0400.0400.0410.0400.0400.0410.0400.0410.0400.0410.0400.0400.0400.0400.0410.0400.0400.0400.0400.0410.0400.0400.0400.0400.0520.0080.0530.0400.0000.0000.0000.0090.0050.0000.0080.0000.0000.0000.0030.0000.0000.0040.0000.0030.0000.0070.0040.0020.0050.0040.0000.0000.0160.0080.0060.0060.0050.0030.0180.0000.0000.0000.0000.0000.0000.0000.0040.0090.0100.0130.0000.0000.0090.0090.0100.0000.0000.0040.0000.0070.0000.0020.0130.0080.0090.0080.0000.0070.0080.0090.0090.0000.0090.0100.0030.0060.0060.0020.0050.0080.0000.0000.0030.0060.0050.0000.0170.0000.0080.0020.0000.0000.0000.0000.0060.0000.0060.0020.0080.0000.0140.0030.0010.0040.0150.0060.0100.0110.0130.0100.0100.0080.0090.0000.0080.0000.0000.0000.0050.0150.0090.0060.0000.0040.0000.0030.0050.0090.0000.0000.0050.0030.0110.0070.0220.0010.0070.0020.0130.0130.0280.0360.0080.0070.0070.0070.0000.0020.0000.0010.0000.0031.0001.0000.0490.0180.0230.0170.0200.0200.2130.0340.0661.0000.018
state_top_WA0.0330.1420.0190.0130.0200.0220.0200.0340.0290.0320.0150.0600.1420.0350.0350.0370.0350.0350.0350.0350.0350.0350.0400.0350.0350.0350.0350.0350.0350.0350.0350.0350.0350.0350.0350.0350.0330.0330.0000.0290.0350.0080.0080.0090.0070.0070.0070.0070.0070.0070.0070.0070.0070.0070.0070.0070.0080.0070.0070.0070.0070.0070.0070.0070.0070.0110.0070.0080.0110.0070.0070.0110.0070.0080.0070.0090.0070.0070.0070.0070.0070.0110.0110.0090.0090.0090.0080.0130.0100.0070.0070.0070.0100.0080.0080.0120.0110.0090.0080.0070.0090.0100.0090.0090.0080.0100.0120.0070.0100.0100.0070.0070.0080.0070.0080.0070.0070.0080.0070.0110.0080.0110.0070.0070.0070.0080.0070.0070.0070.0080.0170.0090.0070.0130.0100.0100.0130.0100.0100.0120.0110.0090.0080.0100.0080.0090.0070.0080.0090.0070.0090.0080.0100.0100.0070.0070.0090.0070.0070.0070.0090.0070.0090.0090.0000.0090.0050.0060.0010.0000.0000.0120.0080.0160.0230.0060.0060.0060.0060.0040.0050.0000.0030.0020.0051.0001.0000.0330.0120.0160.0110.0140.0130.1430.0230.0440.0181.000

Missing values

2023-12-31T13:25:46.653628image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-31T13:25:55.686832image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-31T13:26:19.032236image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

account_idis_current_customeris_self_serviceis_arr_over_12kcompany_revenue_bucketcountrystateindustry_groupedhas_crossbeam_datacrossbeam_product1_customercrossbeam_product2_customercrossbeam_product3_customercrossbeam_product4_customercrossbeam_product5_customercrossbeam_product6_customercrossbeam_product7_customercrossbeam_product8_customercrossbeam_product9_customercrossbeam_product10_customercrossbeam_product11_customercrossbeam_product12_customercrossbeam_product13_customercrossbeam_product14_customercrossbeam_product15_customercrossbeam_product16_customercrossbeam_product17_customercrossbeam_product18_customercrossbeam_product19_customercrossbeam_product20_customercrossbeam_product21_customercrossbeam_product22_customercrossbeam_product23_customerdnb_founded_time_groupedhas_hg_datahg_product_27hg_product_28hg_product_29hg_product_30hg_product_31hg_product_32hg_product_33hg_product_34hg_product_35hg_product_36hg_product_37hg_product_38hg_product_39hg_product_40hg_product_41hg_product_42hg_product_43hg_product_44hg_product_45hg_product_46hg_product_47hg_product_48hg_product_49hg_product_50hg_product_51hg_product_52hg_product_53hg_product_54hg_product_55hg_product_56hg_product_57hg_product_58hg_product_59hg_product_60hg_product_61hg_product_62hg_product_63hg_product_64hg_product_65hg_product_66hg_product_67hg_product_68hg_product_69hg_product_70hg_product_71hg_product_72hg_product_73hg_product_74hg_product_75hg_product_76hg_product_77hg_product_78hg_product_79hg_product_80hg_product_81hg_product_82hg_product_83hg_product_84hg_product_85hg_product_86hg_product_87hg_product_88hg_product_89hg_product_90hg_product_91hg_product_92hg_product_93hg_product_94hg_product_95hg_product_96hg_product_97hg_product_98hg_product_99hg_product_100hg_product_101hg_product_102hg_product_103hg_product_104hg_product_105hg_product_106hg_product_107hg_product_108hg_product_109hg_product_110hg_product_111hg_product_112hg_product_113hg_product_114hg_product_115hg_product_116hg_product_117hg_product_118hg_product_119hg_product_120hg_product_121hg_product_122hg_product_123hg_product_124hg_product_125hg_product_126hg_product_127hg_product_128hg_product_129hg_product_130hg_product_131hg_product_132hg_product_133hg_product_134hg_product_135hg_product_136hg_product_137hg_product_138hg_product_139hg_product_140hg_product_141hg_product_142hg_product_143hg_product_144hg_product_145hg_product_146hg_product_147hg_product_148country_topstate_topcountry_top_AUcountry_top_BRcountry_top_CAcountry_top_DEcountry_top_FRcountry_top_GBcountry_top_INcountry_top_Missingcountry_top_NLcountry_top_Othercountry_top_USstate_top_CAstate_top_COstate_top_FLstate_top_GAstate_top_ILstate_top_MAstate_top_Missingstate_top_NYstate_top_Otherstate_top_TXstate_top_WAindustry_1. Tech - Computer systems design and related servicesindustry_2. Tech - Software Publisherindustry_3. Financeindustry_4. Consultingindustry_5. Retailindustry_6. Manufacturingindustry_7. Wholesale Tradeindustry_8. Healthcareindustry_9. Otherindustry_nanrevenue_Over 10Brevenue_Under 100Mrevenue_Under 10Brevenue_Under 10Mrevenue_Under 1Brevenue_Under 1Mrevenue_nanfounded_After 2000founded_Before 2000founded_nan
0001PY000002SuJDYA0False0.00.0Under 10BCHNaN9. OtherFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0Before 2000False0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0OtherMissingFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalse
10011G00000nEkzYQASFalse0.00.0NaNNLNaN9. OtherFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0After 2000False0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NLMissingFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueTrueFalseFalse
20011G00000qFM5EQAWFalse0.00.0NaNNaNNaNNaNFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0MissingMissingFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseTrue
30011G00000tKtSbQAKFalse0.00.0NaNRONaN9. OtherFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0After 2000False0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0OtherMissingFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueTrueFalseFalse
40011G00000tLivvQACFalse0.00.0NaNINNaN3. FinanceFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0Before 2000False0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0INMissingFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseTrueFalse
50011G00000uoGZKQA2False0.00.0NaNUSNaN9. OtherFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0USMissingFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseTrue
60011G00000ufx52QAAFalse0.00.0NaNMXNaN9. OtherFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0Before 2000False0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0OtherMissingFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseTrueFalse
70011G00000xA4EKQA0False0.00.0NaNBRNaNNaNFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0BRMissingFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseTrue
80011G0000120dN1QAIFalse0.00.0NaNPHNaN9. OtherFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0OtherMissingFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseTrue
90011G0000135y3dQAAFalse0.00.0NaNUSMN9. OtherFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNFalse0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0USOtherFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseTrue
account_idis_current_customeris_self_serviceis_arr_over_12kcompany_revenue_bucketcountrystateindustry_groupedhas_crossbeam_datacrossbeam_product1_customercrossbeam_product2_customercrossbeam_product3_customercrossbeam_product4_customercrossbeam_product5_customercrossbeam_product6_customercrossbeam_product7_customercrossbeam_product8_customercrossbeam_product9_customercrossbeam_product10_customercrossbeam_product11_customercrossbeam_product12_customercrossbeam_product13_customercrossbeam_product14_customercrossbeam_product15_customercrossbeam_product16_customercrossbeam_product17_customercrossbeam_product18_customercrossbeam_product19_customercrossbeam_product20_customercrossbeam_product21_customercrossbeam_product22_customercrossbeam_product23_customerdnb_founded_time_groupedhas_hg_datahg_product_27hg_product_28hg_product_29hg_product_30hg_product_31hg_product_32hg_product_33hg_product_34hg_product_35hg_product_36hg_product_37hg_product_38hg_product_39hg_product_40hg_product_41hg_product_42hg_product_43hg_product_44hg_product_45hg_product_46hg_product_47hg_product_48hg_product_49hg_product_50hg_product_51hg_product_52hg_product_53hg_product_54hg_product_55hg_product_56hg_product_57hg_product_58hg_product_59hg_product_60hg_product_61hg_product_62hg_product_63hg_product_64hg_product_65hg_product_66hg_product_67hg_product_68hg_product_69hg_product_70hg_product_71hg_product_72hg_product_73hg_product_74hg_product_75hg_product_76hg_product_77hg_product_78hg_product_79hg_product_80hg_product_81hg_product_82hg_product_83hg_product_84hg_product_85hg_product_86hg_product_87hg_product_88hg_product_89hg_product_90hg_product_91hg_product_92hg_product_93hg_product_94hg_product_95hg_product_96hg_product_97hg_product_98hg_product_99hg_product_100hg_product_101hg_product_102hg_product_103hg_product_104hg_product_105hg_product_106hg_product_107hg_product_108hg_product_109hg_product_110hg_product_111hg_product_112hg_product_113hg_product_114hg_product_115hg_product_116hg_product_117hg_product_118hg_product_119hg_product_120hg_product_121hg_product_122hg_product_123hg_product_124hg_product_125hg_product_126hg_product_127hg_product_128hg_product_129hg_product_130hg_product_131hg_product_132hg_product_133hg_product_134hg_product_135hg_product_136hg_product_137hg_product_138hg_product_139hg_product_140hg_product_141hg_product_142hg_product_143hg_product_144hg_product_145hg_product_146hg_product_147hg_product_148country_topstate_topcountry_top_AUcountry_top_BRcountry_top_CAcountry_top_DEcountry_top_FRcountry_top_GBcountry_top_INcountry_top_Missingcountry_top_NLcountry_top_Othercountry_top_USstate_top_CAstate_top_COstate_top_FLstate_top_GAstate_top_ILstate_top_MAstate_top_Missingstate_top_NYstate_top_Otherstate_top_TXstate_top_WAindustry_1. Tech - Computer systems design and related servicesindustry_2. Tech - Software Publisherindustry_3. Financeindustry_4. Consultingindustry_5. Retailindustry_6. Manufacturingindustry_7. Wholesale Tradeindustry_8. Healthcareindustry_9. Otherindustry_nanrevenue_Over 10Brevenue_Under 100Mrevenue_Under 10Brevenue_Under 10Mrevenue_Under 1Brevenue_Under 1Mrevenue_nanfounded_After 2000founded_Before 2000founded_nan
5095890011G00000gXzeFQASFalse0.00.0Under 100MUSNY3. FinanceTrue-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0NaNTrue-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0USNYFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrue
5095900011G00000hC5jNQASFalse0.00.0Under 1BDENaN4. ConsultingTrue-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0After 2000True-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.01.01.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.01.01.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0DEMissingFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseTrueFalseFalse
5095910011G00000gZTuJQAWFalse0.00.0Under 10BTRNaN6. ManufacturingTrue-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0NaNTrue-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.01.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.01.0-1.0-1.0-1.01.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0OtherMissingFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseTrue
5095920011G00000gY0h8QACFalse0.00.0Under 1BUSUT3. FinanceTrue-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0NaNTrue-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0USOtherFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseTrue
509593001Ho000014lVGpIAMFalse0.00.0NaNCHNaN3. FinanceTrue-1.0-1.0-1.0-1.01.01.01.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0After 2000True-1.01.0-1.0-1.01.01.01.01.0-1.01.0-1.01.01.0-1.01.01.0-1.01.01.0-1.01.01.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.01.0-1.0-1.0-1.0-1.01.01.0-1.01.0-1.01.01.01.01.01.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.01.0-1.0-1.0-1.01.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.01.01.01.01.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.01.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.01.01.01.0-1.0-1.0-1.01.0-1.0-1.01.0-1.0-1.0-1.0-1.01.0-1.01.01.0-1.0-1.0-1.01.01.0-1.0-1.0-1.0OtherMissingFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueTrueFalseFalse
5095940011G00000f7hy5QAAFalse0.00.0Under 10BIENaN9. OtherTrue-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0Before 2000True-1.0-1.0-1.0-1.01.0-1.01.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.01.01.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.01.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.01.01.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.01.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0OtherMissingFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalse
5095950011G00000gY2yjQACFalse0.00.0Under 1BUSWI8. HealthcareTrue-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0Before 2000True-1.01.0-1.0-1.01.01.01.01.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.01.0-1.01.01.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.01.0-1.01.01.01.0-1.0-1.0-1.01.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.01.0-1.0-1.0-1.0-1.01.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.01.0-1.01.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.01.0-1.0-1.0-1.01.01.0-1.0-1.0-1.0USOtherFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseTrueFalse
5095960011G00000mULinQAGFalse0.00.0Under 10BUSAZ9. OtherTrue-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0Before 2000True-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.01.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.01.01.0-1.01.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.01.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0USOtherFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalse
5095970011G00000hC4rZQASFalse0.00.0Under 10BNLNaN3. FinanceTrue-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0After 2000True-1.0-1.0-1.0-1.0-1.01.01.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.01.01.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.01.0-1.0-1.0-1.0-1.0NLMissingFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseFalse
5095980011G00000w8kSyQAIFalse0.00.0Under 100MGBNaN9. OtherTrue-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0After 2000True-1.0-1.0-1.0-1.01.01.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.01.01.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.01.0-1.0-1.0-1.01.0-1.0-1.0-1.0-1.0GBMissingFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalse